Multimodal fusion of fingerprint and iris

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

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.

Motivation
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 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

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).
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7 MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

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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

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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.

•Biometrics
•Fingerprint Recognition System.
•Iris Recognition System.
•Multimodal Biometrics
•Fusion Techniques
•Iris Localization
Related Theory

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.
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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

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.

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

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:

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

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

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

Problem Definition

 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.

• Technology Used.
• Model Development .
• Hybrid Wavelet based
Feature Extraction [80].
• Fingerprint Feature
Extraction & Matching.
• Snapshots.
Design Implementation and
Analysis

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

Model Development
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Fig: 4 Architecture of the proposed system.

Hybrid Wavelet based Feature
Extraction [80]
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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

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Multiresolution Analysis using Hybrid Wavelet and Proposed Method.

Fingerprint Feature Extraction &
Matching.
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Table :1 Fingerprint Samples Taken from Same User and Corresponding ROI
User Fingerprint 1 Fingerprint 2 Fingerprint 3 Fingerprint 4

Snapshots
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Fig: 6 User enrollment for Iris.

Iris Localization Process

Iris normalization

Test image and Standard
Image

Iris feature vector using hybrid
wavelet type - I and type – II.

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Multiresolution analysis of iris ROI for feature vector extraction (image
0-180).MF of Fingerprint & Iris using HW based FV Bhavesh H.Pandya

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Multi resolution analysis of iris ROI for feature vector extraction (image

21-Jan-1537
Multi resolution analysis of iris ROI for feature vector extraction (image

User enrollment for
Fingerprint.

Ten Fingerprint sample

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Project Demonstration

Result and Discussion

Performance Metrics
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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

Iris Recognition Results
21-Jan-1543
Fig :7 Performance Comparison of Kekre’s & Haar Wavelets for Iris
Recognition

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

Fingerprint & Iris Score based Fusion
21-Jan-1545
Fig : 8 Performance Comparison of Fingerprint & Iris Multimodal
Fusion.

Conclusion

 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.

 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.

Future Scope

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.

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[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.

Publications

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

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Special thanks to Dr. Vinayak Bharadi
for his valuable guidance.. .!

Multimodal fusion of fingerprint and iris

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