This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
Analysis of Digital Image Forgery Detection using Adaptive Over-Segmentation ...IRJET Journal
This document proposes two methods for detecting forged regions in digital images: adaptive over-segmentation and feature point matching. Adaptive over-segmentation divides the host image into irregular, non-overlapping blocks to reduce computational complexity compared to overlapping blocks. Feature points are then extracted from each block using SIFT and matched between blocks to identify labeled feature points that indicate suspected forgery regions. Finally, a forgery region extraction algorithm processes the labeled feature points and applies morphological operations to detect the forged regions in the host image. The proposed methods aim to address limitations of prior blocked-based forgery detection techniques by improving efficiency and ability to handle geometric transformations of forged areas.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses the implementation of a fingerprint matching algorithm. It begins with an introduction to fingerprint recognition and matching. It then discusses the literature on fingerprint matching algorithms. The proposed algorithm involves three main steps: fingerprint pre-processing (including enhancement and binarization), minutiae extraction, and post-processing (including false minutiae removal). Experimental results on the FVC2002 database show that the proposed algorithm has a lower matching time and better accuracy rates compared to an existing method. The algorithm is concluded to be effective for fingerprint image identification.
Fingerprint image enhancement is the key process in IAFIS systems. In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced. A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image. This paper presents an experimental summary of enhancing fingerprint images using Gabor filters. Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, π/2, π/4 and 3π/4 radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features.
Fingerprint Image Compression using Sparse Representation and Enhancement wit...Editor IJCATR
A technique for enhancing decompressed fingerprint image using Wiener2 filter is proposed. First compression is done by sparse representation. Compression of fingerprint is necessary for reducing the memory consumption and efficient transfer of fingerprint images. This is very essential for the application which includes access control and forensics. So the fingerprint image is compressed using sparse representation. In this technique, first dictionary is constructed for patches of fingerprint images. Then a fingerprint is selected and the coefficients are obtained and encoded. Thus the compressed fingerprint is obtained. But when the fingerprint is reconstructed, it is affected by noise. So Wiener2 filter is used to filter the noise in the image. The ridge and bifurcation count is extracted from decompressed and enhanced fingerprints. The experiment result shows that the enhanced fingerprint image preserves more bifurcation than decompressed fingerprint image. The future analysis can be considered for preserving ridges.
A new technique to fingerprint recognition based on partial windowAlexander Decker
1) The document presents a new technique for fingerprint recognition based on analyzing a partial window around the core point of a fingerprint.
2) The technique first locates the core point of a fingerprint, then determines a window around the core point. Features are extracted from this window and input into an artificial neural network (ANN) to recognize fingerprints.
3) The technique aims to reduce computation time for fingerprint recognition by focusing the analysis on a partial window rather than the whole fingerprint image.
Analysis of Digital Image Forgery Detection using Adaptive Over-Segmentation ...IRJET Journal
This document proposes two methods for detecting forged regions in digital images: adaptive over-segmentation and feature point matching. Adaptive over-segmentation divides the host image into irregular, non-overlapping blocks to reduce computational complexity compared to overlapping blocks. Feature points are then extracted from each block using SIFT and matched between blocks to identify labeled feature points that indicate suspected forgery regions. Finally, a forgery region extraction algorithm processes the labeled feature points and applies morphological operations to detect the forged regions in the host image. The proposed methods aim to address limitations of prior blocked-based forgery detection techniques by improving efficiency and ability to handle geometric transformations of forged areas.
This document discusses a face recognition system that uses Gabor feature extraction and neural networks. 40 Gabor filters are applied to images to extract features with different orientations. Fiducial points are identified based on maximum intensity points and distances between points are calculated. These distances are compared to a pre-defined database to recognize faces. A neural network with multiple layers is used to classify faces based on the Gabor filter outputs. The system was able to accurately detect faces in test images by comparing distances between fiducial points to the stored database.
This document describes a face recognition technique that uses a hybrid of principal component analysis (PCA) and an artificial neural network. PCA is used to extract global features of the entire face and local features of the eyes, nose, and mouth regions. These features are used as inputs to an artificial neural network for training and testing. The technique aims to leverage both global and local features for face recognition while reducing computation time compared to local-feature-only approaches.
Analysis of Image Fusion Techniques for fingerprint Palmprint Multimodal Biom...IJERA Editor
The multimodal Biometric System using multiple sources of information has been widely recognized. However computational models for multimodal biometrics recognition have only recently received attention. In this paper the fingerprint and palmprint images are chosen and fused together using image fusion methods. The biometric features are subjected to modality extraction. Different fusion methods like average fusion, minimum fusion, maximum fusion, discrete wavelet transform fusion and stationary wavelet transformfusion are implemented for the fusion of extracting modalities. The best fused template is analyzed by applying various fusion metrics. Here the DWT fused image provided better results.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
Review of three categories of fingerprint recognition 2prjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as minutiae points require less storage than images but it is more time-consuming than other methods. The correlation-based method matches entire fingerprint images and handles poor quality prints better but is computationally expensive. Pattern-based matching compares fingerprint swirl/loop patterns but requires consistent image alignment. Challenges include enhancing low-quality images and improving feature extraction, matching, and alignment algorithms.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image type water meter character recognition based on embedded dspcsandit
1) The document presents a method for automatic water meter character recognition using image processing techniques and a DSP processor. Images of water meters are collected via camera and processed using segmentation, binarization, and filtering.
2) Characters are recognized using a projection method by matching projection curves to templates, with additional methods used to recognize similar characters. Recognition accuracy of over 95% was achieved.
3) The system was tested on a hardware platform and was able to automatically read meters, replacing manual reading and improving efficiency while reducing costs.
IRJET- Face Spoof Detection using Machine Learning with Colour FeaturesIRJET Journal
This document proposes a machine learning approach to detect face spoofing using color features. It extracts local texture features from face images converted to different color spaces like RGB, HSV, and YCbCr. These features along with distortion features are used to train an SVM classifier to detect genuine faces and spoofed faces like photos and videos. Prior work on face spoofing detection mainly focused on intensity and avoided chroma components, but the chroma components in color spaces are effective for distinguishing real and fake faces. The proposed approach extracts color-based texture features to help identify spoofed faces.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Authentication of Degraded Fingerprints Using Robust Enhancement and Matching...IDES Editor
Biometric system is an automated method of
identifying a person based on physiological, biology and
behavioural traits. The physiological traits in include face,
fingerprint, palm print and iris which remains permanent
throughout an individual life time. In the event that these
physiological traits have been degraded then the
authentication of an individual becomes very difficult. The
challenge of restoring a degraded physiological image to an
acceptable appearance in order to authenticate an individual
is very enormous. Fingerprint is one of the most extensively
used biometric systems for authentication in areas where
security is of high importance. This is due to their accuracy
and reliability. However, extracting features out of degraded
fingerprints is the most challenging in order to obtain high
fingerprint matching performance. This paper endeavors to
enhance the clarity of fingerprint minutiae, removing false
minutiae and improve the matching performance using a
robust Gabor Filtering Technique (GFT) and Back Propagation
Artificial Neural Network (BP-ANN). The experiments showed
a remarkable improvement in the performance of the system.
Digital Image Forgery Detection Using Improved Illumination Detection ModelEditor IJMTER
Image processing methods are widely used in advertisement, magazines, blogs, website,
television and more. When the digital images took their role, Happening of crimes and escaping from
the crimes happened becomes easier. To be with lawful, No one should be punished for not
commencing a crime, to help them this application can be used. The identification using color edge
method will give a exact detection of the crime and the forgeries that has been done in the digital
image.
Image composition or splicing methods are used to discover the image forgeries. The approach is
machine-learning- based and requires minimal user interaction and this technique is applicable to
images containing two or more people and requires no expert interaction for the tampering decision.
The obtained result by the classification performance using an SVM (Super Vector Machine) metafusion classifier and It yields detection rates of 86% on a new benchmark dataset consisting of 200
images, and 83% on 50 images that were collected from the Internet.
The further improvements can be achieved when more advanced illuminant color estimators become
available. Bianco and Schettini has proposed a machine-learning based illuminant estimator
particularly for faces which would help us in this for more accurate prediction. Effective skin
detection methods have been developed in the computer vision literature and this method also helps
us, in detecting pornography compositions which, according to forensic practitioners, have become
increasingly common nowadays.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
This document contains a collection of aptitude questions gathered from various websites. It includes 30 questions covering topics like percentages, time/work problems, coding problems, logic puzzles, and spatial reasoning. The questions are multiple choice and have answers provided at the end. The sender is offering to share more questions and collections on C, C++, Java, and general interview questions if requested.
Fingerprints are the most popular and reliable biometric feature used for security applications due to their stability and uniqueness. There are over 120 fingerprint patterns classified, with the top five being arch, tented arch, left loop, right loop, and whorl. Fingerprint recognition involves enrollment, verification, and identification, with the main techniques being minutiae extraction and pattern matching. Minutiae extraction represents fingerprints by local ridge endings and bifurcations, while pattern matching compares basic fingerprint patterns between a stored template and candidate print.
The document proposes a novel dynamic anisotropic pore model (DAPM) and adaptive pore extraction method for fingerprint biometrics. DAPM models pores using two parameters - scale and orientation - that vary based on local ridge features. This provides a more flexible and accurate pore representation than previous isotropic models. The adaptive method estimates ridge orientation and frequency in image blocks, then detects pores by applying the parameterized DAPM model. This block-wise approach reduces computation costs compared to pixel-level methods, while still enabling accurate pore extraction. The proposed DAPM and adaptive method aim to improve fingerprint matching by incorporating more discriminative Level 3 minutiae features.
This document discusses fingerprint recognition using neural networks. It begins with an overview of fingerprints and their unique patterns. It then describes the components of a pattern recognition system for fingerprints, including image acquisition, edge detection, thinning, feature extraction, and classification. Neural networks are proposed for fingerprint recognition because they can learn from examples and process large amounts of data quickly. Other applications of neural networks discussed include character recognition, image compression, stock market prediction, and more. The document concludes by noting that fingerprints will continue to be a reliable biometric for human identification.
This document describes a fingerprint recognition system that uses minutiae-based matching. It extracts minutiae features like ridge endings and bifurcations from fingerprints. These minutiae templates are stored in a database along with unique IDs. The system then performs verification by comparing a given fingerprint's minutiae to templates in the database. It also allows identification by searching the entire database for any matching templates without an ID. The proposed system aims to improve matching performance by reconstructing fingerprints' orientation fields from minutiae and incorporating this additional information into the matching process.
My PptIntroduction to 3G, GSM, GPRS, EDGE NetworkARVIND SARDAR
The document provides an introduction to 3G mobile networks including GSM, GPRS and EDGE. It discusses the evolution from 1G to 2G to 3G networks, with 2G introducing GSM and 2.5G being GPRS. 3G aimed to support higher data speeds. GPRS offered speeds up to 114kbps, EDGE up to 384kbps, and UMTS/HSDPA up to 14Mbps. It then describes the key components and architecture of GSM and GPRS networks.
This document describes a face recognition technique that uses a hybrid of principal component analysis (PCA) and an artificial neural network. PCA is used to extract global features of the entire face and local features of the eyes, nose, and mouth regions. These features are used as inputs to an artificial neural network for training and testing. The technique aims to leverage both global and local features for face recognition while reducing computation time compared to local-feature-only approaches.
Analysis of Image Fusion Techniques for fingerprint Palmprint Multimodal Biom...IJERA Editor
The multimodal Biometric System using multiple sources of information has been widely recognized. However computational models for multimodal biometrics recognition have only recently received attention. In this paper the fingerprint and palmprint images are chosen and fused together using image fusion methods. The biometric features are subjected to modality extraction. Different fusion methods like average fusion, minimum fusion, maximum fusion, discrete wavelet transform fusion and stationary wavelet transformfusion are implemented for the fusion of extracting modalities. The best fused template is analyzed by applying various fusion metrics. Here the DWT fused image provided better results.
An efficient method for recognizing the low quality fingerprint verification ...IJCI JOURNAL
In this paper, we propose an efficient method to provide personal identification using fingerprint to get better accuracy even in noisy condition. The fingerprint matching based on the number of corresponding minutia pairings, has been in use for a long time, which is not very efficient for recognizing the low quality fingerprints. To overcome this problem, correlation technique is used. The correlation-based fingerprint verification system is capable of dealing with low quality images from which no minutiae can be extracted reliably and with fingerprints that suffer from non-uniform shape distortions, also in case of damaged and partial images. Orientation Field Methodology (OFM) has been used as a preprocessing module, and it converts the images into a field pattern based on the direction of the ridges, loops and bifurcations in the image of a fingerprint. The input image is then Cross Correlated (CC) with all the images in the cluster and the highest correlated image is taken as the output. The result gives a good recognition rate, as the proposed scheme uses Cross Correlation of Field Orientation (CCFO = OFM + CC) for fingerprint identification.
The document proposes a hybrid technique using Anisotropic Scale Invariant Feature Transform (A-SIFT) and Robust Ensemble Support Vector Machine (RESVM) to accurately identify faces in images. A-SIFT improves upon traditional SIFT by applying anisotropic scaling to extract richer directional keypoints. Keypoints are processed with RESVM and hypothesis testing to increase accuracy above 95% by repeatedly reprocessing images until the threshold is met. The technique was tested on similar and different facial images and achieved better results than SIFT in retrieval time and reduced keypoints.
Edge detection of herbal plants is a set of mathematical methods which aim at identifying points in a digital image at which the image brightness changes sharply and has discontinuities. They are defined as the set of curved line segments termed edges. Effective edge detection for microscopic image of herbal plant is proposed through this paper which compares the edge detected images and then performs further segmentation. Comparison between Sobel operator, Prewitt, Canny and Robert cross operators is performed. Our method after efficient edge detection performs Gabor filter and K-means clustering to procure a better image. It is then subjected to further segmentation. Experimental methods in our proposed algorithm show that our method achieves a better edge detection as compared to other edge detector operators. Our proposed algorithm provides the maximum PSNR value of 43.684 amongst the other commercial edge detection operators.
Review of three categories of fingerprint recognition 2prjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as minutiae points require less storage than images but it is more time-consuming than other methods. The correlation-based method matches entire fingerprint images and handles poor quality prints better but is computationally expensive. Pattern-based matching compares fingerprint swirl/loop patterns but requires consistent image alignment. Challenges include enhancing low-quality images and improving feature extraction, matching, and alignment algorithms.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Image type water meter character recognition based on embedded dspcsandit
1) The document presents a method for automatic water meter character recognition using image processing techniques and a DSP processor. Images of water meters are collected via camera and processed using segmentation, binarization, and filtering.
2) Characters are recognized using a projection method by matching projection curves to templates, with additional methods used to recognize similar characters. Recognition accuracy of over 95% was achieved.
3) The system was tested on a hardware platform and was able to automatically read meters, replacing manual reading and improving efficiency while reducing costs.
IRJET- Face Spoof Detection using Machine Learning with Colour FeaturesIRJET Journal
This document proposes a machine learning approach to detect face spoofing using color features. It extracts local texture features from face images converted to different color spaces like RGB, HSV, and YCbCr. These features along with distortion features are used to train an SVM classifier to detect genuine faces and spoofed faces like photos and videos. Prior work on face spoofing detection mainly focused on intensity and avoided chroma components, but the chroma components in color spaces are effective for distinguishing real and fake faces. The proposed approach extracts color-based texture features to help identify spoofed faces.
FINGERPRINT MATCHING USING HYBRID SHAPE AND ORIENTATION DESCRIPTOR -AN IMPROV...IJCI JOURNAL
Fingerprint recognition is a promising factor for the Biometric Identification and authentication process.
Fingerprints are broadly used for personal identification due to its feasibility, distinctiveness, permanence,
accuracy and acceptability. This paper proposes a way to improve the Equal Error Rate (EER) in
fingerprint matching techniques in the domain of hybrid shape and orientation descriptor. This type of
fingerprint matching domain is popular due to capability of filtering false and strange minutiae pairings.
EER is calculated by using FMR and FNMR to check the performance of proposed technique.
Fingerprints are imprints formed by friction
ridges of the skin and thumbs. They have long been used for
identification because of their immutability and individuality.
Immutability refers to the permanent and unchanging character
of the pattern on each finger. Individuality refers to the
uniqueness of ridge details across individuals; the probability
that two fingerprints are alike is about 1 in 1.9x1015. In despite of
this improvement which is adopted by the Federal Bureau of
Investigation (FBI), the fact still is “The larger the fingerprint
files became, the harder it was to identify somebody from their
fingerprints alone. Moreover, the fingerprint requires one of the
largest data templates in the biometric field”. The finger data
template can range anywhere from several hundred bytes to over
1,000 bytes depending upon the level of security that is required
and the method that is used to scan one's fingerprint. For these
reasons this work is motivated to present another way to tackle
the problem that is relies on the properties of Vector
Quantization coding algorithm.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Rotation Invariant Face Recognition using RLBP, LPQ and CONTOURLET TransformIRJET Journal
This document discusses rotation invariant face recognition using three feature extraction techniques: Rotated Local Binary Pattern (RLBP), Local Phase Quantization (LPQ), and Contourlet transform. It first extracts features from input face images using these three techniques. It then applies Linear Discriminant Analysis to reduce the feature dimensions. Finally, it uses k-Nearest Neighbors classification to perform face recognition on the Jaffe dataset. Experimental results show that the face recognition accuracy without LDA is 99.06% and increases to 100% when LDA is used for feature dimension reduction.
Authentication of Degraded Fingerprints Using Robust Enhancement and Matching...IDES Editor
Biometric system is an automated method of
identifying a person based on physiological, biology and
behavioural traits. The physiological traits in include face,
fingerprint, palm print and iris which remains permanent
throughout an individual life time. In the event that these
physiological traits have been degraded then the
authentication of an individual becomes very difficult. The
challenge of restoring a degraded physiological image to an
acceptable appearance in order to authenticate an individual
is very enormous. Fingerprint is one of the most extensively
used biometric systems for authentication in areas where
security is of high importance. This is due to their accuracy
and reliability. However, extracting features out of degraded
fingerprints is the most challenging in order to obtain high
fingerprint matching performance. This paper endeavors to
enhance the clarity of fingerprint minutiae, removing false
minutiae and improve the matching performance using a
robust Gabor Filtering Technique (GFT) and Back Propagation
Artificial Neural Network (BP-ANN). The experiments showed
a remarkable improvement in the performance of the system.
Digital Image Forgery Detection Using Improved Illumination Detection ModelEditor IJMTER
Image processing methods are widely used in advertisement, magazines, blogs, website,
television and more. When the digital images took their role, Happening of crimes and escaping from
the crimes happened becomes easier. To be with lawful, No one should be punished for not
commencing a crime, to help them this application can be used. The identification using color edge
method will give a exact detection of the crime and the forgeries that has been done in the digital
image.
Image composition or splicing methods are used to discover the image forgeries. The approach is
machine-learning- based and requires minimal user interaction and this technique is applicable to
images containing two or more people and requires no expert interaction for the tampering decision.
The obtained result by the classification performance using an SVM (Super Vector Machine) metafusion classifier and It yields detection rates of 86% on a new benchmark dataset consisting of 200
images, and 83% on 50 images that were collected from the Internet.
The further improvements can be achieved when more advanced illuminant color estimators become
available. Bianco and Schettini has proposed a machine-learning based illuminant estimator
particularly for faces which would help us in this for more accurate prediction. Effective skin
detection methods have been developed in the computer vision literature and this method also helps
us, in detecting pornography compositions which, according to forensic practitioners, have become
increasingly common nowadays.
Image De-noising and Enhancement for Salt and Pepper Noise using Genetic Algo...IDES Editor
Image Enhancement through De-noising is one of
the most important applications of Digital Image Processing
and is still a challenging problem. Images are often received
in defective conditions due to usage of Poor image sensors,
poor data acquisition process and transmission errors etc.,
which creates problems for the subsequent process to
understand such images. The proposed Genetic filter is capable
of removing noise while preserving the fine details, as well as
structural image content. It can be divided into: (i) de-noising
filtering, and (ii) enhancement filtering. Image Denoising
and enhancement are essential part of any image processing
system, whether the processed information is utilized for visual
interpretation or for automatic analysis. The Experimental
results performed on a set of standard test images for a wide
range of noise corruption levels shows that the proposed filter
outperforms standard procedures for salt and pepper removal
both visually and in terms of performance measures such as
PSNR.Genetic algorithms will definitely helpful in solving
various complex image processing tasks in the future.
This document contains a collection of aptitude questions gathered from various websites. It includes 30 questions covering topics like percentages, time/work problems, coding problems, logic puzzles, and spatial reasoning. The questions are multiple choice and have answers provided at the end. The sender is offering to share more questions and collections on C, C++, Java, and general interview questions if requested.
Fingerprints are the most popular and reliable biometric feature used for security applications due to their stability and uniqueness. There are over 120 fingerprint patterns classified, with the top five being arch, tented arch, left loop, right loop, and whorl. Fingerprint recognition involves enrollment, verification, and identification, with the main techniques being minutiae extraction and pattern matching. Minutiae extraction represents fingerprints by local ridge endings and bifurcations, while pattern matching compares basic fingerprint patterns between a stored template and candidate print.
The document proposes a novel dynamic anisotropic pore model (DAPM) and adaptive pore extraction method for fingerprint biometrics. DAPM models pores using two parameters - scale and orientation - that vary based on local ridge features. This provides a more flexible and accurate pore representation than previous isotropic models. The adaptive method estimates ridge orientation and frequency in image blocks, then detects pores by applying the parameterized DAPM model. This block-wise approach reduces computation costs compared to pixel-level methods, while still enabling accurate pore extraction. The proposed DAPM and adaptive method aim to improve fingerprint matching by incorporating more discriminative Level 3 minutiae features.
This document discusses fingerprint recognition using neural networks. It begins with an overview of fingerprints and their unique patterns. It then describes the components of a pattern recognition system for fingerprints, including image acquisition, edge detection, thinning, feature extraction, and classification. Neural networks are proposed for fingerprint recognition because they can learn from examples and process large amounts of data quickly. Other applications of neural networks discussed include character recognition, image compression, stock market prediction, and more. The document concludes by noting that fingerprints will continue to be a reliable biometric for human identification.
This document describes a fingerprint recognition system that uses minutiae-based matching. It extracts minutiae features like ridge endings and bifurcations from fingerprints. These minutiae templates are stored in a database along with unique IDs. The system then performs verification by comparing a given fingerprint's minutiae to templates in the database. It also allows identification by searching the entire database for any matching templates without an ID. The proposed system aims to improve matching performance by reconstructing fingerprints' orientation fields from minutiae and incorporating this additional information into the matching process.
My PptIntroduction to 3G, GSM, GPRS, EDGE NetworkARVIND SARDAR
The document provides an introduction to 3G mobile networks including GSM, GPRS and EDGE. It discusses the evolution from 1G to 2G to 3G networks, with 2G introducing GSM and 2.5G being GPRS. 3G aimed to support higher data speeds. GPRS offered speeds up to 114kbps, EDGE up to 384kbps, and UMTS/HSDPA up to 14Mbps. It then describes the key components and architecture of GSM and GPRS networks.
This document is a final project report submitted by Sailendra Sagar Patra and Sandeep Kumar Panda to Biju Patnaik University of Technology in partial fulfillment of their B.Tech degree. The report details their work on developing a fingerprint recognition system based on minutiae matching. It describes the algorithms used for fingerprint enhancement, segmentation, minutiae extraction and matching. Results demonstrating the different steps are also provided and compared.
Fingerprint Registration Using Zernike Moments : An Approach for a Supervised...CSCJournals
In this work, we deal with contactless fingerprint biometrics. More specifically, we are interested in solving the problem of registration by taking into consideration some constraints such as finger rotation and translation. In the proposed method, the registration requires: (1) a segmentation technique to extract streaks, (2) a skeletonization technique to extract the center line streaks and (3) and landmarks extraction technique. The correspondence between the sets of control points, is obtained by calculating the descriptor vector of Zernike moments on a window of size RxR centered at each point. Comparison of correlation coefficients between the descriptor vectors of Zernike moments helps define the corresponding points. The estimation of parameters of the existing deformation between images is performed using RANSAC algorithm (Random SAmple Consensus) that suppresses wrong matches. Finally, performance evaluation is achieved on a set of fingerprint images where promising results are reported.
COMPARATIVE ANALYSIS OF MINUTIAE BASED FINGERPRINT MATCHING ALGORITHMSijcsit
Biometric matching involves finding similarity between fingerprint images.The accuracy and speed of the
matching algorithmdetermines its effectives. This researchaims at comparing two types of matching
algorithms namely(a) matching using global orientation features and (b) matching using minutia triangulation.The comparison is done using accuracy, time and number of similar features. The experiment is conducted on a datasets of 100 candidates using four (4) fingerprints from each candidate. The data is sampled from a mass registration conducted by a reputable organization in Kenya.Theresearch reveals that fingerprint matching based on algorithm (b) performs better in speed with an average of 38.32 milliseconds
as compared to matching based on algorithm (a) with an average of 563.76 milliseconds. On accuracy,algorithm(a) performs better with an average accuracy of 0.142433 as compared to algorithm (b) with an average accuracy score of 0.004202.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Iaetsd latent fingerprint recognition and matchingIaetsd Iaetsd
The document discusses latent fingerprint recognition and matching using statistical texture analysis. It proposes extracting three statistical features from fingerprints - entropy coefficient from intensity histogram, correlation coefficient using Wiener filter, and wavelet energy coefficient from 5-level wavelet decomposition. These features are used to represent fingerprints mathematically and provide efficient fingerprint recognition. Existing fingerprint recognition methods are also discussed, including those based on minutiae matching and dealing with nonlinear distortions. However, these do not fully address the problem. The proposed statistical analysis approach can provide more accurate recognition results.
A Review Paper on Fingerprint Image Enhancement with Different MethodsIJMER
This document summarizes various techniques that have been used for fingerprint image enhancement in previous research. It discusses enhancement techniques in the spatial and frequency domains, as well as neural network-based and fuzzy-based approaches. Specifically, it reviews 12 different fingerprint enhancement algorithms proposed between 1994 and 2010. These algorithms use approaches such as directional filtering, Gabor filtering, median filtering, and genetic algorithms. The document evaluates each method and compares their performance based on metrics like minutiae extraction accuracy and false match rates. Overall, the document provides an overview of the state-of-the-art in fingerprint image enhancement techniques.
A New Deep Learning Based Technique To Detect Copy Move Forgery In Digital Im...IRJET Journal
This document proposes a new deep learning technique to detect copy move forgery in digital images. It uses a VGG16 CNN model to extract feature vectors from image blocks. Euclidean distance is used to measure similarity between feature vectors and detect matching blocks, indicating potential forgery. The proposed method is evaluated on the CoMoFoD dataset and achieves higher F1-scores than ResNet50 and EfficientNet models, detecting forged regions more accurately.
Enhanced Latent Fingerprint Segmentation through Dictionary Based ApproachEditor IJMTER
The accuracy of latent finger print matching compared to roll and plain finger print
matching is significantly lower due to background noise, poor ridge quality and overlapping
structured noise in latent images. In this paper the proposed algorithm is dictionary-based approach
for automatic segmentation and enhancement towards the goal of achieving “lights out” latent
identifications system. Total variation decomposition model with L1 fidelity regularization in latent
finger print image remove background noise. A coarse to fine strategy is used to improve robustness
and accuracy. It improves the computational efficiency of the algorithm.
This document describes a fingerprint authentication system for ATMs. It discusses capturing fingerprint images using an optical sensor, extracting minutiae features like ridge endings and bifurcations, and matching fingerprints by comparing minutiae triplets. The system aims to provide biometric security for ATM transactions by verifying a user's identity based on their fingerprint and PIN code. It proposes encrypting fingerprint images during transmission and extracting encryption keys from the images to protect biometric data.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It provides a concise way to represent templates and allows for faster matching by first comparing singular point information before minutiae points.
This document describes a new methodology for improving the accuracy of fingerprint verification systems. It proposes detecting singular points like core and delta points, and indexing templates based on the occurrence of delta points relative to the core point. Experiments on the FVC2006 database show the proposed method achieves higher recognition rates and lower false acceptance and rejection rates compared to existing minutiae-based matching techniques, especially for distorted images. It introduces a new way of storing templates as strings of numbers that encode singular point and minutiae information to enable faster matching.
Biometric system works on behavioral and physiological biometric parameters to spot a person. Every fingerprint contains distinctive options and its recognizing system primarily works on native ridge feature local ridge endings, minutiae, core point, delta, etc. However, fingerprint pictures have poor quality thanks to variations in skin and impression conditions. In personal identification, fingerprint recognition is taken into account the foremost outstanding and reliable technique for matching with keep fingerprints within the information. Minutiae extraction is additional essential step in fingerprint matching. This paper provides plan regarding numerous feature extraction and matching algorithms for fingerprint recognition systems and to seek out that technique is additional reliable and secure.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document summarizes a research paper that proposes an efficient method for recognizing low quality fingerprints using cross correlation. It begins with an introduction to fingerprint identification and verification. It then describes the proposed system, which uses orientation field methodology as a preprocessing step to convert images to orientation patterns. The input image is cross correlated with images in a cluster, and the highest correlated image is output. Experimental results on 1000 fingerprints from a public database showed the method achieved an 85% recognition rate. The paper concludes the cross correlation of orientation fields is an effective approach for fingerprint identification, especially for low quality images.
Image Features Matching and Classification Using Machine LearningIRJET Journal
This document presents a research paper that proposes a new methodology for image feature matching and classification using machine learning. The paper aims to improve accuracy and robustness in feature extraction and matching between digital images. The proposed methodology extracts features from images using machine learning, matches common features between images, and classifies objects. It is evaluated based on precision, recall, and F1-score, and shows improved performance over traditional Scale Invariant Feature Transform (SIFT) techniques on tested datasets with different objects. The proposed approach extracts fewer features and takes less computation time than traditional methods.
Hybrid fingerprint matching algorithm for high accuracy and reliabilityeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Smqt Based Fingerprint Enhancement And Encryption For Border Crossing Securit...theijes
Biometric passport (e-passport) is to prevent the illegitimate entry of traveler into a particular country and border the use of counterfeit documents by more accurate identification of an individual. The electronic passport, as it is sometimes called, represents a bold proposal in the procedure of two new technologies: cryptography authentication protocols and biometrics (face, fingerprints, palm prints and iris).The goal of the adoption of the electronic passport is not only to accelerate processing at border crossings, but also to increase safety measures. Adaptive fingerprint enhancement method is used to enhance the fingerprint image. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. The adaptive fingerprint enhancement method comprises five processing blocks. 1) Pre-processing; 2) global analysis; 3) local analysis; and 4) matched filtering; 4) Image segmentation. In the pre-processing and local analysis blocks, a nonlinear dynamic range adjustment method, SMQT is used. These processing blocks yield an improved and new adaptive fingerprint image processing method. . For assuring security cryptography can be used with enhancement technique for encrypting the enhanced image so as to provide additional protection against fake. For this an image encryption approach using stream ciphers based on non linear filter generator along with AES encryption is used here. In this work a novel image encryption scheme using stream cipher algorithm based on nonlinear filter generator is considered. In this work a novel image encryption scheme is proposed based on stream cipher algorithm using pseudorandom generator with filtering function. This algorithm makes it possible to cipher and decipher images by guaranteeing a maximum security. The proposed cryptosystem is based on the use the linear feedback shift register (LFSR) with large secret key filtered by resilient function whose resiliency order, algebraic degree and nonlinearity attain Siegenthaler’s and Sarkar, al.’s bounds. This scheme is simple and highly efficient.
A Survey on Fingerprint Identification for Different Orientation Images.IRJET Journal
This document summarizes several papers on fingerprint identification techniques for images with different orientations. It discusses methods like discrete wavelet transform, Gabor filters, dual tree complex wavelet transform, and backpropagation neural networks. Evaluation of these techniques show they can extract features from low quality and rotated images but may struggle with large datasets or high levels of distortion. The document concludes different applications could select techniques or their combination based on required accuracy and robustness to orientation.
A Comparative Study of Fingerprint Matching AlgorithmsIRJET Journal
This document summarizes and compares several fingerprint matching algorithms. It begins with an introduction to fingerprint-based identification and authentication, describing how fingerprints provide a unique biometric for verifying identity. The document then reviews three specific fingerprint matching algorithms: 1) Ratio of Relational Distance Matching, which uses minutiae points and distance ratios to match fingerprints; 2) K-Nearest Neighbor Minutiae Clustering, which clusters fingerprint graphs using KNN before matching; and 3) Minutiae Extraction and Matching Algorithm, which extracts minutiae points through a multi-step process of binarization, thinning, connecting, and margin increasing. The document concludes by noting each algorithm has advantages and disadvantages depending on the application
Review of three categories of fingerprint recognitionprjpublications
This document reviews three categories of fingerprint recognition techniques: correlation-based, minutiae-based, and pattern-based. Minutiae-based matching is the most popular as it uses ridge endings and bifurcations, but it is time-consuming. Pattern-based matching uses a virtual core point and pattern points for alignment. Correlation-based matching superimposes images and computes pixel correlations but is computationally expensive. Challenges include handling low quality images and improving feature extraction and matching accuracy and speed.
Review of three categories of fingerprint recognition 2prj_publication
This document reviews three categories of fingerprint recognition techniques: minutiae-based matching, pattern based matching, and correlation-based matching. Minutiae-based matching is the most popular and analyzes ridge endings and bifurcations. Pattern based matching uses fingerprint patterns like loops and whorls. Correlation-based matching overlays images and computes pixel correlations. The document also discusses challenges with fingerprint identification like low quality images and evaluates different enhancement and feature extraction methods.
Similar to Enhanced Thinning Based Finger Print Recognition (20)
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Enhanced Thinning Based Finger Print Recognition
1. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
DOI: 10.5121/ijci.2013.2204 33
Enhanced Thinning Based Finger Print Recognition
[1]
Parul Mishra, [2]
Ajit Kumar Shrivastava, [3]
Amit Saxena
[1]
Department of CSE, Truba Institute of Engg. and Information Technology, Bhopal,
M.P. , INDIA
Mishra.parul2009@gmail.com
[2]
Dean Academics,Truba Institute of Engg. and Information Technology, Bhopal, M.P. ,
INDIA
ajitshrivastava@rediffmail.com
[3]
Head of the Department, Truba Institute of Engg. and Information Technology,
Bhopal, M.P. , INDIA
amitsaxena@trubainstitute.ac.in
Abstract
This paper is the implementation of fingerprint recognition system in which the matching is done using the
Minutiae points. The methodology is the extracting & applying matching procedure on the Minutiae points
between the sample fingerprint & fingerprint under question. The main functional blocks of this system
follows steps of Image Thinning, Image Segmentation, Minutiae (feature) point Extraction, & Minutiae
point Matching. The procedure of Enhanced Thinning included for the purpose of decreasing the size of the
memory space used by the fingerprint image database.
Keywords
Minutiae; Fingerprint; Segmentation; Image Thinning
1.Introduction
Fingerprint Recognition can be defined as the automated method of verifying a match between
two human fingerprints. A finger print refers to the unique impression left by the friction ridges
found on the inner surface of a finger or a thumb. In order to match two fingerprints the samples
should be analysed and compared on the basis of some special features called Minutia points.
There are two major minutia features that can be found on fingerprint ridges: Ridge Ending and
Ridge Bifurcation. [1] A good quality fingerprint typically contains 40-100 minutiae [2, 3], as
shown in the figure 1.
Figure 1 Finger Print
2. International Journal on Cybernetics & Informatics ( IJCI) Vol.2, No.2, April2013
34
Fortunately, controlled, scientific testing initiatives are not limited within the biometrics
community to fingerprint recognition. Other biometric modalities have been the target of
excellent evaluation efforts as well. The (US) National Institute of Standards and Technology
(NIST) has sponsored scientifically-controlled tests of text-independent speaker recognition
algorithms for a number of years and, more recently, of facial recognition technologies as well.
In pattern recognition system, Feature Extraction (in general) is the process extracting
information from the input which is useful for determining its category. In the case of fingerprints
a natural choice are features based directly on the fingerprint ridges and ridge-valley structure.
However, the effectiveness of a feature extraction can be determined by the image quality.
Consequently, fingerprint image enhancement has become a necessary and common step after
image acquisition and before feature extraction in most AFIS. Following, binarization, feature
extraction and matching algorithms are executed on the enhanced image. The fingerprint
enhancement can be employed on, both the gray-scale, and binary images, in spatial or frequency
domain. In this paper we propose method based on Enhanced thinning of Images of original
input gray-scale fingerprint image in frequency domain.
The rest of this paper progresses as follow. In Section II, Background and Literature survey
presented and its characteristics in frequency domain are briefly described. In Section III,
proposed methodology is explained. Parameters of proposed technique as well as results of
enhancement obtained from available database sets are shown in Section IV, and conclusion is
given in Section V.
TABLE I
PRIMARY CONCERNS IN FINGERPRINT IMAGE RECOGNITION
a) Clarity: It is very important for the image to be clear before extracting the minutiae
points. Therefore Enhancement Techniques are used to obtain high and accurate match
scores.
b) Noise Reduction: By calculating the ROI (Region of Interest), background information
without effective ridges is eliminated.
c) Reduced Problem Space: A threshold is set on the basis of match scores for the
application of Enhanced Thinning where noticeable increase in matching percentage can
be seen.
d) Matching scores: Higher matching scores can be obtained by applying Enhanced
Thinning.
2.Background and Literature Survey
Bana. S, with her colleague in 2011 presents a technique which is based on Minutiae based
matching. This approach mainly depends on extraction of minutiae point from the sample finger
print images and then performs matching based on the number of minutiae pairing among two
fingerprints [4].
In 2009, Andelija M, et.al proposed an algorithm to enhance the fingerprint image based on
adaptive filtering in frequency domain. Due to development of fast algorithms and power of
modern computer systems, the filtering is often done in frequency domain. They propose
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two filter realizations for adaptive filtering in frequency domain, where both of them enhance
fingerprint ridge-valley structure and attenuate existing noise [5].
In 2010, the proposed work for multi-scale and multi-directional recognition of fingerprints by
K.Thaiyalnayaki, with his colleague included effective combination of features. Standard
deviation, kurtosis, and skewness are the features which are included in their work. They apply
the method by analyzing the finger prints with discrete wavelet transform (DWT) [6].
For Binary images a new parallel thinning algorithm was proposed by A. Jagna, in 2010 [7].
This proposed work was designed to solve the problem of excessive erosion and discontinuity in
the images obtained after thinning in ZS and LW algorithm. The execution of this algorithm
includes that the binary image undergoes two iterations of thinning known as two pass parallel
thinning. This process preserves the end points and makes the image one pixel wide. The 8-
neighbour connectivity is also ensured by this algorithm. The reduction in end points is also
observed here. [8-9]. However, the proposed algorithm shows the better performance and
produces more quality images than the previous algorithms.
In 2005, Eun-Kyung Yun, Jin-Hyuk Hong and Sung-Bae proposed an adaptive pre-processing
method, which extracts five features from the fingerprint images, analyses image quality with
Ward’s clustering algorithm, and enhances the images according to the characteristics. Fig.2
shows the overview of the proposed system in this paper. For fingerprint image quality analysis,
it extracts several features in fingerprint images using orientation fields, at first. Clustering
algorithm groups fingerprint images with the features, and the images in each cluster are
analyzed and pre-processed adaptively [10].
Figure 2 Adaptive Enhancing of Finger Print Images
Madhuri and Richa Mishra in 2012 propose a fingerprint recognition technique which uses local
robust features for fingerprint representation and matching. The technique performs well in
presence of rotation and able to carry out recognition in presence of partial fingerprints
[11].
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Fig 3. Finger Print based on Local robust features
Finger Print Matching Techniques
The Fingerprint Matching techniques can be broadly classified into three categories:
Correlation-based matching:
In this technique the correlation between two fingerprint images, for different displacements and
rotations can be computed by superimposing them.
Minutiae-based matching:
It is the most widely used fingerprint matching technique. Here the minutiae are extracted from
the two fingerprints and for increasing the efficiency of the matching process, the extracted
minutiae points are stored in the matrix form. In this technique the template and the input
minutiae sets are matched to generate the matching scores.
Pattern-based (or image-based) matching:
Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop)
between a template and a candidate fingerprint. This requires that the images be aligned in the
same orientation. This algorithm deals with finding a central point and centres on that. In a
pattern-based algorithm, the template contains the size, type and orientation of patterns within the
aligned fingerprint image. Then the graphical comparison is done between the candidate
fingerprint and the template to determine the degree to which they match.
Issues with Existing techniques
Most of the existing fingerprint techniques in literature are based on minutiae points which are
represented using their co-ordinate locations in the image. When test fingerprint image is
rotated with respect to enrolled image or partially available, these techniques face problem in
matching due to change in the co-ordinate locations of the minutiae points and perform very
poorly. These two cases are discussed below.
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Rotated Fingerprint Matching
An example of a rotated fingerprint image is shown in Figure 3(b). We can see that it is difficult
to match minutiae of two images because due to rotation, coordinate locations of all the minutiae
points in Figure 3(b) with respect to Figure 3(a) are changed.
Figure 3 (a) Normal Fingerprint Image, (b) Rotated Fingerprint Image
Partial Fingerprint Matching
Figure 4(b) shows the example of partial fingerprint. We can see that it is difficult to match
minutiae of two images because due to missing part of the fingerprint coordinate locations of all
the minutiae points in Figure 4(b) with respect to Figure 4(a) are changed.
Figure 4 (a) Full Fingerprint (b) Partial Fingerprint Image
3.Proposed Methodology
The Fig 5 shows the proposed methodology. Basic steps involves are written below:
Image Enhancement
Fingerprint Image Enhancement is the first step in the minutiae extraction process. For
better performance of any fingerprint recognition system it is very important that the fingerprint
images should be clear. With clear images higher matching scores can be obtained. As the
fingerprint images are generally obtained from the scanner or other media therefore there is no
guarantee of their good quality. The Image Enhancement techniques can be applied to increase the
contrast between ridges and valleys and for connecting the false broken points of ridges.
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Originally, the enhancement step was supposed to be done using the canny edge detector. But after
trial, it turns out that the result of an edge detector is an image with the borders of the ridges
highlighted. For image enhancement we use:
a. Histogram Equalization
b. Fast Fourier Transform
Figure 5 Flow chart of Proposed Algorithm
Image
Image
Segmentation
Image
Binarization
Minutiae
Extraction
Minutiae
Matching
Save
Extracted
Data
If
Matchin
g % > 30
Accepted
Person
Enhanced
Thinning
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Image Binarization
The binarization step is basically stating the obvious, which is that the true information that
could be extracted from a print is simply binary; ridges vs. valleys. But it is a really
important step in the process of ridge extracting, since the prints are taken as grayscale
images, so ridges, knowing that they’re in fact ridges, still vary in intensity. So, binarization
transforms the image from a 256-level image to a 2-level image where the information remains
same. Typically, an object pixel corresponds to a value of “1” while a background pixel
corresponds to a value of “0.” Finally, depending on a pixel's value (black for 0, white for 1), a
binary image is obtained by colouring each pixel white or black.
Image Segmentation
Image Segmentation is the process of recognizing Region of Interest (ROI) for each fingerprint
image. The area of the image without effective ridges is first discarded since it only holds
background information or noise. Then the bound is sketched out for the remaining effective
area in order to avoid confusion between the minutiae in the bound region with those false
minutiae. The false minutiae are generated as a result of the ridges that are out of the sensor.
There is a two-step method for extracting ROI. Used techniques are:
a) Block Direction Estimation
b) ROI extraction by Morphological Operation
Final Minutiae Extraction
Ridge Thinning is the process of eliminating the redundant pixels of ridges till the width of the
ridges become just one pixel. An iterative, parallel thinning algorithm is used. The algorithm
takes a small window of (3x3) in each scan and marks down the redundant pixels. And after
several scans finally removes all those marked pixels. After this by using Morphological
operations the thinned ridge map is filtered in order to remove some H breaks, isolated points
and spikes. In this step, any single points, whether they are single-point ridges or single-point
breaks in a ridge are eliminated and considered processing noise.
Minutia Marking
After Thinning of the fingerprint ridges, marking minutia points is relatively easy. For extraction
of minutiae, the concept of Crossing Number (CN) is widely used.
In a fingerprint image, for each 3x3 window, the central pixel is said to be a Ridge Branch, if its
value is 1 and has exactly 3 one-value neighbours. [Figure 5.1]
Figure 5.1
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And the central pixel is said to be a Ridge Ending if its value is 1 and has only 1 one-value
neighbour. [Figure 5.2]. Therefore for any pixel P, if Cn(P) = = 1 it is called a Ridge End point
and if Cn(P) = = 3 it is called a Ridge Bifurcation point.
Figure 5.2
Fig 4.3 shown below illustrates a special case that a genuine branch is triple counted. If the
uppermost pixel has value 1 and the rightmost pixel with value 1, have another neighbour outside
the 3x3 window, then the two pixels will be marked as branches but actually only one
branch is located in the small region. So a check routine is added requiring that none of
the neighbours of a branch are branches.
Now D is estimated which is the Average Inter-Ridge Width. The average inter-ridge width can
be defined as the average distance between two neighbouring ridges. It is simple to approximate
the value of D. Scan a row of the thinned ridge image and sums up all the pixels in the
row with value one. Then divide the row length by the above summation to obtain the
inter-ridge width. To provide more accuracy, several other rows and column scans are also
conducted, finally all the inter-ridge widths are averaged to get the D.
In addition to marking the minutiae, all the thinned ridges in the fingerprint image are labelled
with a unique ID for further operation
Figure 5.3
Minutiae Post-Processing
False Minutia Removal
There are some problems that cannot be totally fixed at the pre-processing stage. There are some
problems that are not completely eliminated, caused due to insufficient amount of ink like false
ridge breaks and ridge cross-connections due to over inking. Actually some artefacts are
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introduced at the earlier stages occasionally which later lead to spurious minutia. These false
minutiae affects the accuracy of matching if they are simply regarded as genuine minutiae.
So there should be some methods of removing false minutia which are essential to keep the
fingerprint verification system effective.
Enhanced Thinning
Enhanced Thinning algorithm involves eight neighborhood. However, to preserve the
connectivity is difficult here. Therefore to handle this problem, we use a 3 x 3 mask. The mask
shown in Figure-6 (a & b) indicating the eight neighboring pixels variations. A connectivity value
is the sum of each weight in eight directions. After calculating the value of connectivity and
applying specific conditions, decision can be made whether to delete the object pixel or to
preserve. An essential point is defined as one which includes a connect point and an end point.
The connect point is a point that its removal causes a disconnectivity in 3 x 3 mask. The end point
can be defined as the point having only one of the eight-adjacent points. Proposed algorithm
simply applies the above definitions so that the connectivity of the entire image can be
maintained, to overcome the deficiencies of previous parallel thinning algorithms. The
proposed algorithm consists of two steps i.e., Rule 1, In this step the value of connectivity for
the entire image is calculated step by step and Rule 2 eliminates non-essential pixels step
by step from the entire image. The pixel elimination process can be terminated if all the pixels
are found essential. In Sequential Image Thinning algorithm the retention or deletion of a
(black) pixel p depends upon the configuration of pixels in a local neighborhood containing p,
and the deletion of p in the nth iteration depends upon the operations undergone so far in the (n-
l)th iterations, and on the pixels that are processed in the nth iteration. In Proposed Parallel
Image Thinning Algorithm, the elimination of pixels in the nth iteration would depends only on
the output produced after the (n-l)th iteration, therefore, the processing of all the pixels can be
takes place independently in a parallel manner, and thereby producing better quality thinned
images, without excessive erosion and with 8-connectivity.
Figure 6 (a) Figure 6 (b)
4.Implementation and Result
Implementation of proposed system requires better image quality, in order to produce match
scores with higher accuracy. So concerning with this fact we develop a system which enhances
the fingerprint image to provide clarity.
Background information and processing noise are the two major issues of this Fingerprint
Recognition System. Following are the details of elimination of background information and
processing noise in the proposed system.
X 1 X
X Pi 1
X 1 X
X x x
1 Pi 1
X 1 X
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Figure 6.1 Main GUI of proposed Approach
Figure 6.2 Histogram Equalized Image
Figure 6.3 FFT Enhanced Image
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Figure 6.4 Binarized Image
Figure 6.5 Direction Finded Image
Figure 6.6 ROI extracted Image
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Figure 6.7 Thinned Image
Figure 6.8 Case When Enhanced Thinning Required
Figure 6.9 Results after Enhanced Thinning
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TABLE II
COMPARISION OF RESULTS
Image Previous
Result
Proposed
Result
Img 1 26.66 49.24
Img 2 28.84 51.82
Img 3 24.56 48.56
Img 4 27.21 50.25
5.Discussions and Conclusion
The process of Minutia Extraction plays a very important role in making any Automatic
Fingerprint System reliable. Poor image quality is one of the most influential factors that can
damage the process of locating minutia’s correct location.
Enhanced Thinning based matching algorithm which is being proposed is found to be capable
enough to locate the analogy or similarity between minutiae without adopting exhaustive research.
Also the Enhanced Thinning algorithm is applied only to the images which are falling under a
fixed threshold based on matching percentage so that the bad matching images can be separated
from the good ones and thus the Enhanced thinning is applied only to the images under that
threshold. After applying Enhanced Thinning to the images having low threshold, noticeable
increase in the matching percentage is obtained.
The proposed work can also be improved by working on the accuracy and increasing the efficiency
which can be achieved by applying the various image enhancement techniques or by the hardware
improvements so that the captured images are more correct and valid. This will improve the input
image to the thinning process that will directly reflect the better image quality
References
[1] “Minutiae Detection Algorithm for Fingerprint Recogenition”, IEEE AESS Systems Magazine, 2002.
[2] Jain A., Lin Hong & Sharath Pankanti, “An Indentification System Using Fingerprints”, IEEE
Proceedings, Vol. 85, 1997.
[3] Jain A., Bolle R. & Sharath Pankanti, “ Biometric Peersonal Idetification in Networked Society”,
Kluwer Academic Publishers.
[4] S.Bana, D.Kaur, “Fingerprint Recognition using Image Segmentation”, INTERNATIONAL
JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES, Vol No. 5,
Issue No. 1, 012 – 023
[5] Andelija M, Brankica. M, “An Effective and Robust Fingerprint Enhancement by Adaptive Filtering
in Frequency Domain”, ELEC. ENERG. vol. 22, no. 1, April 2009, 91-104.
[6] K.Thaiyalnayaki, S.S.A.Karim, P. V. Parmar, “Finger Print Recognition using Discrete Wavelet
Transform”, International Journal of Computer Applications (0975 - 8887) , Volume 1 – No. 24,
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[7] A. Jagna, V. Kamakshiprasad, “NEW PARALLEL BINARY IMAGE THINNING ALGORITHM”,
ARPN Journal of Engineering and Applied Sciences, VOL. 5, NO. 4, APRIL 2010
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[8] T .Y. Zhang and C.Y. Suen. 1984. A Fast Parallel Algorithms for Thinning Digital Patterns.
Research Contributions, Communications of the ACM. 27(3): 236-239.
[9] H.E. Lu and P.S.P. Wang. 1985. An improved fast parallel algorithm for thinning digital
patterns. Proc. of the IEEE Conf. on computer vision and pattern recognition. pp. 364-367.
[10] Eun-Kyung Yun, Jin-Hyuk Hong and Sung-Bae Cho, “Adaptive Enhancing of Fingerprint Image with
Image Characteristics Analysis”, AI 2004: Advances in Artificial Intelligence, Lecture Notes in
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[11] M. Mishra, R. Mishra, “Fingerprint Recognition using Robust Local Features”, International Journal
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ISSN: 2277 128X.
Authors:
Mr. Ajit Kumar Shrivastava working as the Dean Academics of Truba Institute of Engineering &
Information Technology, Bhopal. He is having a vast experience in the field of Computer Science and
Academics. He has guided a number of Projects and Dissertations for M. Tech. Students, as well as for UG
Students. He has completed his Engineering (Honors) in Computer Technology, Master of Engineering
(Honors) in Computer Engineering.
Mr. Amit Saxena working as the Head of the Department of Computer Science & Engineering in Truba
Institute of Engineering & Information Technology, Bhopal. He is having Nine years of experience in the
field of Computer Science and Academics. He has guided more than 40 Projects and Dissertations for M.
Tech. Students, as well as for UG Students. He has completed his Engineering (Honors) in Computer
Science & Engineering, Master of Engineering (Honors) in Computer Science Engineering. His area of
research is Security Issues in Mobile Adhoc Network and Behavioral Analysis of Selfish and Malicious
Nodes in MANET.
Parul Mishra has done her Engineering from Shree Institute of Science & Technology, Bhopal (RGPV) in
Computer Science Engineering Branch. She is having an Experience of 1 Year in Academics, worked as an
Assistant Professor (CS/IT) in People’s College of Research & Technology, Bhopal. Currently, she is
pursuing M.Tech. (Final Semester) in Computer Science Engineering Branch from Truba Institute of
Engineering & Information Technology, Bhopal (RGPV). Her areas of interest are Security, Object
Oriented Programming, Operating Systems, Data Base Management Systems, etc. Her topic for research is
Finger Print Recognition.