A Survey on Fingerprint Identification for Different Orientation Images.

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 03 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 2518
A Survey on Fingerprint Identification For different Orientation
Images.
Miss. Kaveri Bhagat1 , Prof. Sushen Gulhane2.
1M.E.(E&TC) & kaveribhagat96@gmail.com
2 Assistant Professor, Electronics & Telecommunication, Dr. D. Y. Patil College of engineering, Maharashtra, India
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Abstract -Most of system uses Biometric toidentifypersons
.So one can use any feature such as it may be face, fingerprint
,hand retina etc. In this paper we focus on fingerprint
recognition. Input image is scanned and then pre-process to
extract feature using different algorithm. In this way by using
fingerprint as input image we process on this image and find
out the person is authorized or not.
Key Words: Fingerprint, DWT, Gabor filter, DTCWT, BPNN,
fingerprint matching
1.INTRODUCTION
Now a days it is necessary to identify authorized
person. For that purpose most of systems uses
Biometrictofindvalidpersons.Usercanusetraditional
methodssuch asusingPINs,password,token,butthese
methods are not much secure. The password or PINs
are access by other person or it can be misplaced so
there will be misuse of this data so instead of that we
can use biometric system for more secure in online
shopping or in other area such as ATMs etc.
The biometric system (bio means life and metric
meansmeasure)basedonfeaturevector.Thesevectors
are derived from behavioral characteristics or
physiological. Biometric system uses Bbehavioral
characteristicsofPersonsuchashandscan,fingerprint,
face scan, Retina scan. Biometrics deals with automatic
recognition of persons based on their behavioral
characteristics and physiological. Biometrics are more
reliable as compared to traditional methods. Out of
thesebehavioralcharacteristicshereweusefingerprint
to identifypersons. Eachpersonhasuniquefingerprint
for twins also. Input image may be any type i.e. inked
image, scan image , etc. Fingerprint have graphical
patternssuch as ridges and valleys, and minutia points.
A fingerprint composed of a sequence of ridges and
valleys which are parallel to each other. The dark lines
are ridges and light areas between ridges are the
valleys.
There are number of techniques are available to
extract feature out of that minutia based method is
popular one. It uses feature vector means extract
feature from database for different orientation and
extract feature from input image also and then
matching algorithm is used here to match images.
Basically fingerprint authentication is used to match
two fingerprint images. There are three basic steps to
recognize persons preprocessing, feature extraction,
matching.
Fig. Fingerprint with core, valleys, ridges
On the inner most ridge core is defined. The different
fingerprint images are described below.
a)Live-scan fingerprint b)Inked fingerprint on paper
c) fingerprint from NRC

2. REVIEW OF LITERATURE
2.1 Alessandra A. Paulino &Anil K. Jain ,“Latent
Fingerprint Matching Using Descriptor-Based Hough
Transform ” IEEE transactions on information forensics
and security, vol. 8, no. 1, january 2013
This paper uses noise characteristics and and small number
of minutiae of latent to their full prints which are stored in
database .In this paper work is done on latent fingerprint. It
uses hough transform method to find similarity between
two fingerprints. It consider both orientation as well as
minutiae. But it has less accuracy when there is overlap
between the latent and rolled prints is small.
2.2 K.Thaiyalnayaki, S. Syed Abdul Karim, P. Varsha
Parmar “Finger Print Recognition using Discrete
Wavelet Transform ” in ©2010 International Journal of
Computer Applications (0975 - 8887) Volume 1 – No. 24
In this paper DWT is used for analyzing the finger
prints .Input image is preprocess to remove noise and
then DWT is applied to extract feature.. Here Canberra
distance metric is used for similarity comparison
between the texture classes. But it is observed that it
can not analyze low resolutionfingerprintimagesand
images with dark back ground.
2.3 Amit Kaul ,A.S. Arora & Sushil Chauhan “Rotation
Invariant Fingerprint Core-Point Detection using DWT”
International Conference on Recent Advances and
Future Trends in InformationTechnology(iRAFIT2012)
Proceedings published in International Journal of
Computer Applications® (IJCA)
In this paper ,to handle rotational variance DWT is used
here which is based on for core-point detection. To locate
the core his paper uses 2D-waveletcoefficientsinhorizontal,
vertical and diagonal direction. And it gives result for
different database with different orientation. This paper
uses Wavelet Transform. But thedrawback ofthismethod it
is not applicable for larger database and development of
rotation invariant.
2.4 Ju Cheng Yang and Dong Sun Park “Fingerprint
Verification Based on Invariant Moment Features
and Nonlinear BPNN ” International Journal of
Control, Automation, and Systems, vol. 6,no. 6, pp.
800-808, December 2008.
This paper uses set of invariant moment featuresanda
nonlinear Back Propagation Neural Network (BPNN)
verifier. This paper overcome the drawback of
traditional methods. It has two stages off line and on
line stage. On line stage is used for testing input image,
and off line stage is used for template processing. This
paper uses neural network, BPNN ,invariant moment
features. The result of this system gives higher
accuracy and higher matching speed.
2.5 Jossy P. George, Abhilashs.K.,ChethanaM.D.andRaja
K. B. “Performance Analysis of FingerprintIdentification
Using Different Levels of DTCWT ” 2012 International
Conference on Information and Computer Applications
(ICICA 2012) IPCSIT vol. 24 (2012) © (2012) IACSIT
Press, Singapore.
In this paper different level of DTCWTareusedforanalysisof
fingerprint for identification. In this propose system input
image is cropped and resized to apply DTCWT. By applying
DTCWT on input fingerprint we will get coefficient to form
features. To obtain two parts of the images, i.e., real and
imaginary part DTCWT generates complex coefficients by
usinga dual tree of wavelet filters. Different levels of DTCWT
are used here out of that level 7 has good recognition rate.
2.6 Zin Mar Win and Myint Myint Sein “Robust
Fingerprint Recognition System using Orientation and
Texture features ” 2nd International Conference on
Computer Science and Information Technology
(ICCSIT'2012) Singapore April 28-29, 2012
This paper prefer gabor filter to capture local and global
texture data from poor images as well as incompleteimages.
By combining orientation features and the local texture
pattern obtained using a bank of Gabor filters A hybrid
fingerprint matching algorithm is developed. Here gabor
filter is used to extract orientation information and unique
frequency information. So input image is filtered using the
set of gabor filters. According to experimental results, this
method gives improved accuracyof recognitionsystem and
it is robust to image distortion and image rotation. This
algorithm is effective and efficient for rotated images and
low quality.

3. CONCLUSIONS
Papers discussed above provide various methods for
detection of orientation based fingerprintrecognition.Above
paper gives a number of methods to extract the feature and
matching algorithms. Each method have it’s own advantages
and drawback also. According to application We can select
any one of them or combination of them for best result.
REFERENCES
[1] Alessandra A. Paulino &Anil K. Jain ,“Latent Fingerprint
Matching Using Descriptor-Based Hough Transform ”
IEEE transactions on informationforensicsandsecurity,
vol. 8, no. 1, january 2013.
[2] K.Thaiyalnayaki, S. Syed Abdul Karim, P. Varsha Parmar
“Finger Print Recognition using Discrete Wavelet
[3] Amit Kaul ,A.S. Arora & Sushil Chauhan “Rotation
Invariant Fingerprint Core-Point Detection using DWT”
International Conference on Recent Advances and
Future Trends in InformationTechnology(iRAFIT2012)
Proceedings published in International Journal of
Computer Applications® (IJCA)
[4] Ju Cheng Yang and Dong Sun Park “Fingerprint
Verification Based on Invariant Moment Features and
Nonlinear BPNN ” International Journal of Control,
Automation, and Systems, vol. 6,no. 6, pp. 800-808,
December 2008.
[5] Jossy P. George, Abhilashs. K. ,Chethana M.D.andRaja K.
B. “Performance Analysis of Fingerprint Identification
Using Different Levels of DTCWT ” 2012 International
Conference on Information and Computer Applications
(ICICA 2012) IPCSIT vol. 24 (2012) © (2012) IACSIT
Press, Singapore.
[6] Zin Mar Win and Myint Myint Sein “Robust Fingerprint
Recognition System using Orientation and Texture
features ” 2nd International Conference on Computer
Science and Information Technology (ICCSIT'2012)
Singapore April 28-29, 2012.

A Survey on Fingerprint Identification for Different Orientation Images.

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