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Prepared by Bhavesh H.Pandya
Guided by: Dr. Vinayak Bharadi
Registration No: Thakur/86
Multimodal Fusion of Fingerprint and
Iris using Hybrid Wavelet based
Feature vector
21-Jan-151 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Flow of Presentation
 Introduction
 Literature Survey
 Related Theory
 Problem Definition
 Design Implementation
 Result and Discussion
 Conclusions
 Future scope
 References
 Publication
21-Jan-152 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Importance of Project.
Motivation.
21-Jan-153
Introduction
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Importance of Project
 Fingerprint & Iris features are extracted using
multilevel decomposition of fingerprint image
using a new family of wavelet called kekre’s
wavelet and the iris features are extracted using
hybrid wavelet type 1, type -2. In this project KNN
classifier used for unimodal fingerprint recognition
and multi-instance iris recognition. Feature vector
of iris and fingerprint are combined using decision
fusion technique.
21-Jan-154 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Motivation
21-Jan-155
 Biometrics comprises methods for uniquely
recognizing humans based upon one or more intrinsic
physical or Behavioral traits.
 In computer science, in particular, biometrics is used
as a form of identity access management and access
control.
 It is also used to identify individuals in groups that are
under surveillance [1].
 By using biometrics it is possible to establish an
identity based on who you are, rather than by what
you possess, such as an ID card, or what you
remember, such as a password.
 In some applications, biometrics may be used to
supplement ID cards and passwords therebyMF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Literature Survey
21-Jan-156 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Sr.N
o
Paper Title Description
1 An Introduction to
Biometrics
Recognition [3]
•In this paper [3], Biometric recognition or, simply,
biometrics refers to the automatic recognition of
individuals based on their physiological and behavioral
characteristics.
•The results demonstrated that biometrics refers to
automatic recognition of an individual based on her
behavioral and/or physiological characteristics.
•Biometrics-based systems also have some limitations
that may have adverse implications for the security of a
system.
2 Iris Recognition
Using Discrete
Cosine Transform
and Kekre’s Fast
Codebook
Generation
Algorithm [71]
•In this paper [71], an iris recognition system based on
vector quantization and its performance is compared
with the Discrete Cosine Transform (DCT).
•The proposed VQ based system does not need any
pre-processing and segmentation of the iris.
•For vector quantization author used Kekre’s Fast
Codebook Generation Algorithm (KFCG).
21-Jan-15
7 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-158
Sr.No Paper Title Description
3 Fingerprint – Iris
Fusion based
Identification
System using a
Single Hamming
Distance Matcher
[70]
•In this paper [70] author proposed a framework for
multimodal biometric fusion based on utilization of a
single matcher implementation for both modalities.
•The proposed framework is designed to provide
improved performance over the unimodal systems.
4 Multimodal
Biometric
Identification for
Large User
Population Using
Fingerprint, Face
and Iris
Recognition[24]
•This paper [24] overviews and discusses the various
scenarios that are possible in multimodal biometric
systems using fingerprint, face and iris recognition, the
levels of fusion that are possible and the integration
strategies that can be adopted to fuse information and
improve overall system accuracy.
5 Multimodal
Biometrics: Need
for Future Security
Systems [72]
•In this paper [72] author explained different aspects of
biometric identification systems, their types, current
architectures, future architecture and efforts towards
the development of common framework for biometric
identification.MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-159
Sr.No Paper Title Description
6 Fingerprint
Recognition Using
Wavelet Features
[74]
•The wavelet features are extracted directly from the
gray-scale fingerprint image with no pre-processing
(i.e. image enhancement, directional filtering, ridge
segmentation, and ridge thinning and minutiae
extraction). The proposed method has been tested on
a small fingerprint database using the k-nearest
neighbour (k-NN) classifier.
7 An Iris Recognition
System Using
Phase-Based
Image Matching
[75]
•In this paper, author consider the problem of
designing a compact phase based iris recognition
algorithm especially suitable for hardware
implementation.
•The prototype system fully utilizes state-of-the-art
DSP (Digital Signal Processor) technology to achieve
real-time iris recognition capability within a compact
hardware module.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
•Biometrics
•Fingerprint Recognition System.
•Iris Recognition System.
•Multimodal Biometrics
•Fusion Techniques
•Iris Localization
Related Theory
21-Jan-1510 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Biometrics
 Biometrics is the science by which we measure
the physiological and behavioral characteristics of
a person.
 History of Biometrics device.
 Biometrics systems are becoming popular as a
measure to identify human being by measuring
one’s physiological or behavioral characteristics.
 Biometrics identifies the person by what the
person is rather than what the person carries,
unlike the conventional authorization systems like
smart cards.
 Unlike the possession-based and knowledge-
based personal identification schemes, the 21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Biometrics Characteristics
12
Characteristics Meaning
Universality Each person should have the characteristic.
Uniqueness Indicates how well the biometric separates individuals
from another.
Permanence Measures how well a biometric resists aging and other
variance over time.
Collectability Ease of acquisition for measurement.
Performance Accuracy, speed, and robustness of technology used.
Acceptability Degree of approval of a technology.
Circumventio
n
Ease of use of a substitute.
21-Jan-15
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Types of Biometrics
Physiological
 DNA
 Ear
 Face
 Facial, hand, and hand vein
 Fingerprint
 Gait
 Hand and finger geometry
21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Types of Biometrics(cntd)
 Iris
 Keystroke
 Odor
 Palm print
 Retinal scan
Behavioral
 Signature
 Voice
21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Types of Biometrics(cntd)
Fig : 1 Examples of biometric characteristics: (a) DNA, (b) ear, (c) face, (d) facial
thermogram, (e) hand thermogram, (f) hand vein, (g) fingerprint, (h) gait, (i) hand
geometry, (j) iris, (k) palmprint, (l) retina, (m) signature, and (n) voice.
21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Fingerprint Recognition System.
 Fingerprint Pre-processing :
The fingerprint must be pre-processed to remove the
effect of noise, effect of dryness, wetness of the finger
and difference in the applied pressure while scanning
the fingerprint. The pre-processing is a multi-step
process. The different steps in pre-processing are as
follows [29], [30], [31], [38].
 Smoothening Filter
 Intensity Normalization
 Orientation Field Estimation
 Fingerprint Segmentation
 Ridge Extraction
 Thinning
21-Jan-1516 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Iris Recognition System
Generally, iris recognition system consists of four
major steps. They include :
Image acquisition from iris scanner
Iris image pre-processing
Feature extraction
Enrolment / recognition.
 Pre-processing :
 Iris Feature Extraction Methods:
21-Jan-1517 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Multimodal Biometrics
 The most compelling reason to combine different
modalities is to improve the recognition rate &
reliability. This can be done when biometric
features of different biometrics are statistically
independent. There are other reasons to combine
two or more biometrics. One is that the different
biometric modalities might be more appropriate
for the different applications.
 Combinations of Biometric Traits
21-Jan-1518 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Multimodal Biometric Systems
 Multimodal biometric systems take input from
single or multiple sensors measuring two or more
different modalities of biometric characteristics.
 For example, a system combining face and iris
characteristics for biometric recognition
 Multi-algorithmic
 Multi-instance
 Multi-sensorial
21-Jan-1519 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Performance Metrics
 The performance of a biometric system is measured by
different parameters or metrics. The following are used
as performance metrics for biometric systems [1], [2], [3],
[5]:
False Accept Rate or False Match Rate (FAR or FMR)
False Reject Rate or False Non-Match Rate (FRR or
FNMR)
Equal Error Rate, Performance Index and Cprrect
Classification Ratio (PI and CCR)
Other metrics which are related to the sensor devices are
Failure to Enroll Rate (FTE), Failure to Capture Rate (FTC)
& Template Capacity.
 Generally physiological biometric traits are more21-Jan-1520 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Application of Biometrics
 Physical Access
 Virtual Access
 E-commerce Applications
 Covert Surveillance
21-Jan-1521 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Problem Definition
21-Jan-1522 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
 The topic for research is ‘Multimodal Fusion of
Fingerprint and Iris’. It consists of multi instance iris
based biometric systems combined with Fingerprint
based system.
 Main focus of research is to use hybrid wavelet
transforms on enrolled image data to extract the
feature vector from Iris & Fingerprint. The hybrid
wavelet transforms are generated using Discrete
Walsh transform (DWT) and Kekre Wavelets (KW).
Iris localization and feature vector for iris and
fingerprint extracted. Fusion of Iris & Fingerprint
based feature is performed. The system will be
benchmarked by evaluating FAR, FRR, TAR,
TRR,EER and CCR.
21-Jan-1523 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
• Technology Used.
• Model Development .
• Hybrid Wavelet based
Feature Extraction [80].
• Fingerprint Feature
Extraction & Matching.
• Snapshots.
Design Implementation and
Analysis
21-Jan-1524 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Hardware and Software
Requirement
 Hardware Requirement
Processor: Dual Core processor
RAM: 2 GB DDR3 or more
Hard disk: 40 GB or more
 Software Requirement
 Operating System : Windows 7 or higher
 Programming Language : C# .Net
 Development Kit : Visual studio 2012
21-Jan-1525 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Model Development
21-Jan-1526
Fig: 4 Architecture of the proposed system.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Hybrid Wavelet based Feature
Extraction [80]
21-Jan-1527
Fig: 5 Hybrid Wavelet Type I Transform of an Image (a) Original Image &
Hybrid Wavelet Type I Transform Level 1 Components (b) Kekre Wavelets
Components of other Image
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1528
Multiresolution Analysis using Hybrid Wavelet and Proposed Method.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Fingerprint Feature Extraction &
Matching.
21-Jan-1529
Table :1 Fingerprint Samples Taken from Same User and Corresponding ROI
User Fingerprint 1 Fingerprint 2 Fingerprint 3 Fingerprint 4
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Snapshots
21-Jan-1530
Fig: 6 User enrollment for Iris.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Iris Localization Process
21-Jan-1531 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Iris normalization
21-Jan-1532 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Test image and Standard
Image
21-Jan-1533 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Iris feature vector using hybrid
wavelet type - I and type – II.
21-Jan-1534 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1535
Multiresolution analysis of iris ROI for feature vector extraction (image
0-180).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1536
Multi resolution analysis of iris ROI for feature vector extraction (image
90-270).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1537
Multi resolution analysis of iris ROI for feature vector extraction (image
180-360).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
User enrollment for
Fingerprint.
21-Jan-1538 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Ten Fingerprint sample
21-Jan-1539 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1540
Project Demonstration
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Result and Discussion
21-Jan-1541 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Performance Metrics
21-Jan-1542
Total NumberGenuine Fingerprints Rejected as Imposter
Total Number of Genuine Matching Tests Performed
FRR 
Total Number Genuine Fingerprints Accepted
Total Number of Genuine Matching Tests Performed
TAR 
Total Number Imposter Fingerprints Accepted as Genuine
Total Number of Forgery Tests Performed
FAR 
Total Number Imposter Fingerprints Rejected
Total Number of Forgery Tests Performed
TRR 
PI=100-EER
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Iris Recognition Results
21-Jan-1543
Fig :7 Performance Comparison of Kekre’s & Haar Wavelets for Iris
Recognition
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Fingerprint Recognition Results
21-Jan-1544
Sr. Type of Wavelet
PI Accuracy (%) –
CCR
1
Hybrid Wavelet Type
I
78.34
75.23
2
Hybrid Wavelet Type
II
79.32
77.78
3
Kekre’s Wavelets
[78]
90.00
84.40
4 Haar Wavelets [78]
88.00
81.15
Table : Summary of Fingerprint Matching Tests
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Fingerprint & Iris Score based Fusion
21-Jan-1545
Fig : 8 Performance Comparison of Fingerprint & Iris Multimodal
Fusion.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Conclusion
21-Jan-1546 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
 Hybrid wavelets based texture feature extraction
techniques are implemented. Besides this fusion of
biometric traits is also performed.
 Multimodal biometric systems are discussed here.
Fusion of iris and fingerprint is done.
 In this report we are using combination of kekre’s
wavelet and walsh transform hence name given as
hybrid wavelet. Hybrid wavelets have been used
effectively in in this research for texture feature
extraction of fingerprints, & Iris.
 Iris and fingerprint feature vector and extraction
implemented using hybrid wavelet type I & II. Left and
Right iris images are considered separately.
21-Jan-1547 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
 When we compare hybrid wavelet type I with
hybrid wavelet II then we found that accuracy of
hybrid wavelet II is more than hybrid wavelet I.
 Unimodal fingerprint recognition system is fused with
a multi-instance iris recognition system. Decision
level as well as feature level fusion is implemented.
 Kekre’s wavelets are having better texture
information extraction capability as compared to the
Hybrid Wavelets.
 We have used simple Euclidian distance based K-NN
classifier with Five training samples per person. This
is an example of multi-algorithmic biometric fusion.
 Performance of Hybrid Wavelet Type II is better and
they give higher PI & CCR.
21-Jan-1548 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Future Scope
21-Jan-1549 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Some of the further work directions for improvement in
the results and implementation of variants of proposed
systems are as follows.
 In this project, we combined Walsh transform and
Kekre’s Transform but we can combine Haar transform
and DCT, Hartley or any other transform and analyze
their performance.
 We have used KNN classifier in this project but we can
use better classifier like SVM, Neural network etc.
 Hybrid Wavelets can be used for feature extraction of
other biometric traits like Palmprints, Finger-knuckle
print, Face etc.
21-Jan-1550 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
References
21-Jan-1551 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
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Pakistan,2007
[50] C. V. Kameswara Rao and K. Balck, "Finding The Core Point In A Fingerprint", IEEE Transactions on Computers,
Vol. C-27, No. 1, Jan. 1978
[51] S. Chikkukerurr, N. Ratha, ―”Impact of Singular Point Detection on Fingerprint Matching Performance, Automatic
Identification Advanced Technologies”, 2005. Fourth IEEE Workshop on , pp. 207 - 212 , Oct. 2005 277
[52] H. B. Kekre, V. A. Bharadi, "Fingerprint Core Point Detection Algorithm Using Orientation Field Based Multiple
Features", International Journal on Computer Applications (IJCA), France, Vol. 1, No. 15, pp.98-103, Feb. 2010
[53] R. Bahuguna, ―”Fingerprint Verification using Hologram Matched Filtering”, In Proceedings of Biometric
Consortium, 8th Meeting, San Jose, California, June 1996
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1556
[54] M. Eshera, K. S. Fu, ―”A Graph Distance Measure For Image Analysis”, IEEE Transactions on Systems,
Man, and Cybernetics, Vol. 14, No. 3.,pp. 398-408, 1984
[55] M. A. Eshera and K.S. Fu, ―”A Similarity Measure between Attributed Relational Graphs for Image Analysis ,In
Proceedings of 7th International Conference on Pattern Recognition”, pp. 75- 77, Dec. 1984
[56] S. Gold and A. Rangarajan, ―”A Graduated Assignment Algorithm For Graph Matching, IEEE Transactions on
Pattern Analysis and Machine Intelligence”, Vol. 18, No. 4, pp. 377–388, 1996
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Pattern Recognition”, Vol. 23, No. 8, 1990
[58] D. K. Isenor, S. G. Zaky, ―”Fingerprint Identification Using Graph Matching, Journal of Pattern Recognition”, Vol.
19, No. 2, 1986
[59] A. K. Jain, L. Hong, and R. Bolle, ―”On-line fingerprint verification, IEEE Transactions on Pattern Analysis and
Machine Intelligence”, Vol. 19, No. 4, pp. 302–314, Apr. 1997
[60] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, ―”An Identity- Authentication System Using Fingerprints, In
Proceedings of IEEE”, Vol. 85, No. 9, pp. 1365–1388, Sep. 1997
[61] H B Kekre, V A Bharadi, "Palmprint Recognition Using Kekre‘s Wavelet‘s Energy Entropy Based Feature Vector",
Proceedings of International Conference & Workshop on Emerging Trends in Technology 2011, TCET, Mumbai,
India, pp. 39-45, Feb. 2011
[62] H. B. Kekre, V. A. Bharadi, "Finger-Knuckle-Print Region of Interest Segmentation using Gradient Field Orientation
& Coherence ", Proceedings of IEEE International Conference ICETET 2010, India, pp. 43-50, Dec. 2010
[63] H. B. Kekre, T. K. Sarode, V. A. Bharadi, T. Bajaj, S. Chatterjee, M. Bhat, K. Bihani, "A Comparative Study of DCT
and Kekre‘s Median Code Book Generation Algorithm for Face Recognition", ICWET '10 Proceedings of the
International Conference and Workshop on Emerging Trends in Technology, pp. 343-348, Feb. 201
[64] H. B. Kekre, V. A. Bharadi, "Face Recognition using Kekre‘s Wavelets Energy & Performance Analysis of Feature
Vector Variants", Proceedings of ACM International Conference ICWET 2011, Mumbai, India, Vol. 1, pp.39-45,
Feb. 2011
[65] H. B. Kekre, T. K. Sarode, V. A. Bharadi, A. A. Agrawal, R. J. Arora , M. C. Nair, "Performance Comparison of Full
2-D DCT, 2- D Walsh and 1-D Transform over Row Mean and Column Mean for Iris Recognition", Proceedings of
ACM International Conference ICWET 2010, India, pp. 560-567, Feb. 2010MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1557
[66] H. B. Kekre, T. K. Sarode, V. A. Bharadi, A. A. Agrawal, R. J. Arora , M. C. Nair, "Iris Recognition Using
Vector Quantization", Proceedings of IEEE International Conference ICSAP 2010, India, pp. 43-50,
March 2010
67 H. B. Kekre, V. A. Bharadi, "Fingerprint Orientation Field Estimation Algorithm Based on Optimized
Neighborhood Averaging", IEEE International Conference ICETET 2009, India, pp. 543-546, Dec. 2009
68 H. B. Kekre, V. A. Bharadi, "Fingerprint‘s Core Point Detection Using Orientation Field", IEEE International
Conference on Advances in Computing, Control and Telecommunication Technologies (ACT 2009), India, pp.
150 - 152 , Dec. 2009
69 H. B. Kekre, V. A. Bharadi, "Hybrid Multimodal Biometric Recognition Using Kekre‘s Wavelets, 1D Transforms &
Kekre‘s Vector Quantization Algorithms Based Feature Extraction of Face & Iris", International Journal of
Computer Application (IJCA), Vol. 3, No. 3, pp-106-115, March 2011.
70 Asim Baig, Ahmed Bouridane, Fatih Kurugollu “Fingerprint – Iris Fusion based Identification System using a
Single Hamming Distance Matcher”, 2009 Symposium on Bio-inspired Learning and Intelligent Systems for
Security.
71 H B Kekre, V A Bharadi, “Iris Recognition Using Discrete Cosine Transform and Kekre’s Fast Codebook
Generation Algorithm”, International Conference & Workshop on Emerging Trends in Technology 2010 (ICWET
2010), Mumbai, India, 26-27 Feb 2010.
72 H B Kekre, V A Bharadi, “Multimodal Biometrics: Need for Future Security Systems”
73 H B Kekre, V A Bharadi, “Multimodal Biometrics”, International Conference & Workshop on Emerging Trends in
Technology 2010 (ICWET 2010), 26-27 Feb 2010.
74 M. Tico, E. Immonen, P. Ramo, P. Kuosmanen, and J. Saarinen, ―”Fingerprint Recognition Using Wavelet
Features”, IEEE Conference on Biometrics, Vol. II, No. 8, pp. 21–24, 2001.
75 Kazuyuki Miyazawa, Koichi Ito, Takafumi Ao, Koji Kobayashi, Atsushi Katsumata, - “AN IRIS RECOGNITION
SYSTEM USING PHASE-BASED IMAGE MATCHING”, 1-4244-0481-9/06/$20.00 C2006 IEEE.
76 http://phoenix.inf.upol.cz/iris/download/
77 B.Pandya and V.Bharadi , ”Multimodal Fusion of Fingerprint & Iris using Hybrid wavelet based feature vector”, in
International Conference & Workshop on Emerging Trend in Technology Feb.2012.
78 H B Kekre, V A Bharadi, “Biometrics authentication Systems”. In journal feb,2012 published by LAP Lambert
Academic Publishing.
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Publications
21-Jan-1558 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
Journal papers
[1] B.Pandya and V.Bharadi, “Multimodal Fusion of Fingerprint
& Iris using Hybrid wavelet based feature vector” in
International journal of Applied information Systems, Feb
2014.
Conference papers
[1] B.Pandya and V.Bharadi , ”Multimodal Fusion of
Fingerprint & Iris using Hybrid wavelet based feature
vector”, in International Conference & Workshop on
Emerging Trend in Technology Feb.2012.
Book
[1] Multimodal Fusion of Iris and Fingerprint using Hybrid
Wavelets Type I & Type II based Feature Vector.
-Bhavesh Pandya, Dr.Vinayak Bharadi, Dr.H.B.Kekre
Lambert Publication, Germany, ISBN No: 978-3-8484-3243-1
21-Jan-1559 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
21-Jan-1560
Special thanks to Dr. Vinayak Bharadi
for his valuable guidance.. .!
MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

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Multimodal fusion of fingerprint and iris

  • 1. Prepared by Bhavesh H.Pandya Guided by: Dr. Vinayak Bharadi Registration No: Thakur/86 Multimodal Fusion of Fingerprint and Iris using Hybrid Wavelet based Feature vector 21-Jan-151 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 2. Flow of Presentation  Introduction  Literature Survey  Related Theory  Problem Definition  Design Implementation  Result and Discussion  Conclusions  Future scope  References  Publication 21-Jan-152 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 3. Importance of Project. Motivation. 21-Jan-153 Introduction MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 4. Importance of Project  Fingerprint & Iris features are extracted using multilevel decomposition of fingerprint image using a new family of wavelet called kekre’s wavelet and the iris features are extracted using hybrid wavelet type 1, type -2. In this project KNN classifier used for unimodal fingerprint recognition and multi-instance iris recognition. Feature vector of iris and fingerprint are combined using decision fusion technique. 21-Jan-154 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 5. Motivation 21-Jan-155  Biometrics comprises methods for uniquely recognizing humans based upon one or more intrinsic physical or Behavioral traits.  In computer science, in particular, biometrics is used as a form of identity access management and access control.  It is also used to identify individuals in groups that are under surveillance [1].  By using biometrics it is possible to establish an identity based on who you are, rather than by what you possess, such as an ID card, or what you remember, such as a password.  In some applications, biometrics may be used to supplement ID cards and passwords therebyMF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 6. Literature Survey 21-Jan-156 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 7. Sr.N o Paper Title Description 1 An Introduction to Biometrics Recognition [3] •In this paper [3], Biometric recognition or, simply, biometrics refers to the automatic recognition of individuals based on their physiological and behavioral characteristics. •The results demonstrated that biometrics refers to automatic recognition of an individual based on her behavioral and/or physiological characteristics. •Biometrics-based systems also have some limitations that may have adverse implications for the security of a system. 2 Iris Recognition Using Discrete Cosine Transform and Kekre’s Fast Codebook Generation Algorithm [71] •In this paper [71], an iris recognition system based on vector quantization and its performance is compared with the Discrete Cosine Transform (DCT). •The proposed VQ based system does not need any pre-processing and segmentation of the iris. •For vector quantization author used Kekre’s Fast Codebook Generation Algorithm (KFCG). 21-Jan-15 7 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 8. 21-Jan-158 Sr.No Paper Title Description 3 Fingerprint – Iris Fusion based Identification System using a Single Hamming Distance Matcher [70] •In this paper [70] author proposed a framework for multimodal biometric fusion based on utilization of a single matcher implementation for both modalities. •The proposed framework is designed to provide improved performance over the unimodal systems. 4 Multimodal Biometric Identification for Large User Population Using Fingerprint, Face and Iris Recognition[24] •This paper [24] overviews and discusses the various scenarios that are possible in multimodal biometric systems using fingerprint, face and iris recognition, the levels of fusion that are possible and the integration strategies that can be adopted to fuse information and improve overall system accuracy. 5 Multimodal Biometrics: Need for Future Security Systems [72] •In this paper [72] author explained different aspects of biometric identification systems, their types, current architectures, future architecture and efforts towards the development of common framework for biometric identification.MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 9. 21-Jan-159 Sr.No Paper Title Description 6 Fingerprint Recognition Using Wavelet Features [74] •The wavelet features are extracted directly from the gray-scale fingerprint image with no pre-processing (i.e. image enhancement, directional filtering, ridge segmentation, and ridge thinning and minutiae extraction). The proposed method has been tested on a small fingerprint database using the k-nearest neighbour (k-NN) classifier. 7 An Iris Recognition System Using Phase-Based Image Matching [75] •In this paper, author consider the problem of designing a compact phase based iris recognition algorithm especially suitable for hardware implementation. •The prototype system fully utilizes state-of-the-art DSP (Digital Signal Processor) technology to achieve real-time iris recognition capability within a compact hardware module. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 10. •Biometrics •Fingerprint Recognition System. •Iris Recognition System. •Multimodal Biometrics •Fusion Techniques •Iris Localization Related Theory 21-Jan-1510 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 11. Biometrics  Biometrics is the science by which we measure the physiological and behavioral characteristics of a person.  History of Biometrics device.  Biometrics systems are becoming popular as a measure to identify human being by measuring one’s physiological or behavioral characteristics.  Biometrics identifies the person by what the person is rather than what the person carries, unlike the conventional authorization systems like smart cards.  Unlike the possession-based and knowledge- based personal identification schemes, the 21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 12. Biometrics Characteristics 12 Characteristics Meaning Universality Each person should have the characteristic. Uniqueness Indicates how well the biometric separates individuals from another. Permanence Measures how well a biometric resists aging and other variance over time. Collectability Ease of acquisition for measurement. Performance Accuracy, speed, and robustness of technology used. Acceptability Degree of approval of a technology. Circumventio n Ease of use of a substitute. 21-Jan-15 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 13. Types of Biometrics Physiological  DNA  Ear  Face  Facial, hand, and hand vein  Fingerprint  Gait  Hand and finger geometry 21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 14. Types of Biometrics(cntd)  Iris  Keystroke  Odor  Palm print  Retinal scan Behavioral  Signature  Voice 21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 15. Types of Biometrics(cntd) Fig : 1 Examples of biometric characteristics: (a) DNA, (b) ear, (c) face, (d) facial thermogram, (e) hand thermogram, (f) hand vein, (g) fingerprint, (h) gait, (i) hand geometry, (j) iris, (k) palmprint, (l) retina, (m) signature, and (n) voice. 21-Jan-15MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 16. Fingerprint Recognition System.  Fingerprint Pre-processing : The fingerprint must be pre-processed to remove the effect of noise, effect of dryness, wetness of the finger and difference in the applied pressure while scanning the fingerprint. The pre-processing is a multi-step process. The different steps in pre-processing are as follows [29], [30], [31], [38].  Smoothening Filter  Intensity Normalization  Orientation Field Estimation  Fingerprint Segmentation  Ridge Extraction  Thinning 21-Jan-1516 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 17. Iris Recognition System Generally, iris recognition system consists of four major steps. They include : Image acquisition from iris scanner Iris image pre-processing Feature extraction Enrolment / recognition.  Pre-processing :  Iris Feature Extraction Methods: 21-Jan-1517 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 18. Multimodal Biometrics  The most compelling reason to combine different modalities is to improve the recognition rate & reliability. This can be done when biometric features of different biometrics are statistically independent. There are other reasons to combine two or more biometrics. One is that the different biometric modalities might be more appropriate for the different applications.  Combinations of Biometric Traits 21-Jan-1518 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 19. Multimodal Biometric Systems  Multimodal biometric systems take input from single or multiple sensors measuring two or more different modalities of biometric characteristics.  For example, a system combining face and iris characteristics for biometric recognition  Multi-algorithmic  Multi-instance  Multi-sensorial 21-Jan-1519 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 20. Performance Metrics  The performance of a biometric system is measured by different parameters or metrics. The following are used as performance metrics for biometric systems [1], [2], [3], [5]: False Accept Rate or False Match Rate (FAR or FMR) False Reject Rate or False Non-Match Rate (FRR or FNMR) Equal Error Rate, Performance Index and Cprrect Classification Ratio (PI and CCR) Other metrics which are related to the sensor devices are Failure to Enroll Rate (FTE), Failure to Capture Rate (FTC) & Template Capacity.  Generally physiological biometric traits are more21-Jan-1520 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 21. Application of Biometrics  Physical Access  Virtual Access  E-commerce Applications  Covert Surveillance 21-Jan-1521 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 22. Problem Definition 21-Jan-1522 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 23.  The topic for research is ‘Multimodal Fusion of Fingerprint and Iris’. It consists of multi instance iris based biometric systems combined with Fingerprint based system.  Main focus of research is to use hybrid wavelet transforms on enrolled image data to extract the feature vector from Iris & Fingerprint. The hybrid wavelet transforms are generated using Discrete Walsh transform (DWT) and Kekre Wavelets (KW). Iris localization and feature vector for iris and fingerprint extracted. Fusion of Iris & Fingerprint based feature is performed. The system will be benchmarked by evaluating FAR, FRR, TAR, TRR,EER and CCR. 21-Jan-1523 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 24. • Technology Used. • Model Development . • Hybrid Wavelet based Feature Extraction [80]. • Fingerprint Feature Extraction & Matching. • Snapshots. Design Implementation and Analysis 21-Jan-1524 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 25. Hardware and Software Requirement  Hardware Requirement Processor: Dual Core processor RAM: 2 GB DDR3 or more Hard disk: 40 GB or more  Software Requirement  Operating System : Windows 7 or higher  Programming Language : C# .Net  Development Kit : Visual studio 2012 21-Jan-1525 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 26. Model Development 21-Jan-1526 Fig: 4 Architecture of the proposed system. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 27. Hybrid Wavelet based Feature Extraction [80] 21-Jan-1527 Fig: 5 Hybrid Wavelet Type I Transform of an Image (a) Original Image & Hybrid Wavelet Type I Transform Level 1 Components (b) Kekre Wavelets Components of other Image MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 28. 21-Jan-1528 Multiresolution Analysis using Hybrid Wavelet and Proposed Method. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 29. Fingerprint Feature Extraction & Matching. 21-Jan-1529 Table :1 Fingerprint Samples Taken from Same User and Corresponding ROI User Fingerprint 1 Fingerprint 2 Fingerprint 3 Fingerprint 4 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 30. Snapshots 21-Jan-1530 Fig: 6 User enrollment for Iris. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 31. Iris Localization Process 21-Jan-1531 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 32. Iris normalization 21-Jan-1532 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 33. Test image and Standard Image 21-Jan-1533 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 34. Iris feature vector using hybrid wavelet type - I and type – II. 21-Jan-1534 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 35. 21-Jan-1535 Multiresolution analysis of iris ROI for feature vector extraction (image 0-180).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 36. 21-Jan-1536 Multi resolution analysis of iris ROI for feature vector extraction (image 90-270).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 37. 21-Jan-1537 Multi resolution analysis of iris ROI for feature vector extraction (image 180-360).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 38. User enrollment for Fingerprint. 21-Jan-1538 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 39. Ten Fingerprint sample 21-Jan-1539 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 40. 21-Jan-1540 Project Demonstration MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 41. Result and Discussion 21-Jan-1541 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 42. Performance Metrics 21-Jan-1542 Total NumberGenuine Fingerprints Rejected as Imposter Total Number of Genuine Matching Tests Performed FRR  Total Number Genuine Fingerprints Accepted Total Number of Genuine Matching Tests Performed TAR  Total Number Imposter Fingerprints Accepted as Genuine Total Number of Forgery Tests Performed FAR  Total Number Imposter Fingerprints Rejected Total Number of Forgery Tests Performed TRR  PI=100-EER MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 43. Iris Recognition Results 21-Jan-1543 Fig :7 Performance Comparison of Kekre’s & Haar Wavelets for Iris Recognition MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 44. Fingerprint Recognition Results 21-Jan-1544 Sr. Type of Wavelet PI Accuracy (%) – CCR 1 Hybrid Wavelet Type I 78.34 75.23 2 Hybrid Wavelet Type II 79.32 77.78 3 Kekre’s Wavelets [78] 90.00 84.40 4 Haar Wavelets [78] 88.00 81.15 Table : Summary of Fingerprint Matching Tests MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 45. Fingerprint & Iris Score based Fusion 21-Jan-1545 Fig : 8 Performance Comparison of Fingerprint & Iris Multimodal Fusion. MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 46. Conclusion 21-Jan-1546 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 47.  Hybrid wavelets based texture feature extraction techniques are implemented. Besides this fusion of biometric traits is also performed.  Multimodal biometric systems are discussed here. Fusion of iris and fingerprint is done.  In this report we are using combination of kekre’s wavelet and walsh transform hence name given as hybrid wavelet. Hybrid wavelets have been used effectively in in this research for texture feature extraction of fingerprints, & Iris.  Iris and fingerprint feature vector and extraction implemented using hybrid wavelet type I & II. Left and Right iris images are considered separately. 21-Jan-1547 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 48.  When we compare hybrid wavelet type I with hybrid wavelet II then we found that accuracy of hybrid wavelet II is more than hybrid wavelet I.  Unimodal fingerprint recognition system is fused with a multi-instance iris recognition system. Decision level as well as feature level fusion is implemented.  Kekre’s wavelets are having better texture information extraction capability as compared to the Hybrid Wavelets.  We have used simple Euclidian distance based K-NN classifier with Five training samples per person. This is an example of multi-algorithmic biometric fusion.  Performance of Hybrid Wavelet Type II is better and they give higher PI & CCR. 21-Jan-1548 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 49. Future Scope 21-Jan-1549 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 50. Some of the further work directions for improvement in the results and implementation of variants of proposed systems are as follows.  In this project, we combined Walsh transform and Kekre’s Transform but we can combine Haar transform and DCT, Hartley or any other transform and analyze their performance.  We have used KNN classifier in this project but we can use better classifier like SVM, Neural network etc.  Hybrid Wavelets can be used for feature extraction of other biometric traits like Palmprints, Finger-knuckle print, Face etc. 21-Jan-1550 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
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  • 58. Publications 21-Jan-1558 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 59. Journal papers [1] B.Pandya and V.Bharadi, “Multimodal Fusion of Fingerprint & Iris using Hybrid wavelet based feature vector” in International journal of Applied information Systems, Feb 2014. Conference papers [1] B.Pandya and V.Bharadi , ”Multimodal Fusion of Fingerprint & Iris using Hybrid wavelet based feature vector”, in International Conference & Workshop on Emerging Trend in Technology Feb.2012. Book [1] Multimodal Fusion of Iris and Fingerprint using Hybrid Wavelets Type I & Type II based Feature Vector. -Bhavesh Pandya, Dr.Vinayak Bharadi, Dr.H.B.Kekre Lambert Publication, Germany, ISBN No: 978-3-8484-3243-1 21-Jan-1559 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya
  • 60. 21-Jan-1560 Special thanks to Dr. Vinayak Bharadi for his valuable guidance.. .! MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya