Score Level Fusion of Multimodal Biometrics based on Entropy Function

This paper presents the score level fusion of multimodal biometrics using Hanman-Anirban entropy function. Entropy function captures the uncertainty in the scores. The experimental results ascertain that Entropy based score level fusion outperforms over existing methods of score level fusion such as t-norms, sum and max. We have validated our claim on finger-knuckle-print (FKP) dataset consisting of left index, left middle, right index and right middle FKP. The features of FKPs are extracted using the Gabor Wavelet. The implementation is done using MATLAB and the performance of the proposed technique is evaluated using Receiver Operating characteristics (ROC) curve. The proposed score level fusion approach achieves significant improvement in the performance over the individual FKP. We obtain Genuine acceptance rate of 99% with FAR of 0.001 %. General Terms Multimodal Biometrics, Entropy, Authentication.

[1]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[2]  Gian Luca Marcialis,et al.  Serial Fusion of Fingerprint and Face Matchers , 2007, MCS.

[3]  Arun Ross,et al.  Multibiometric systems , 2004, CACM.

[4]  Pingzhi Fan,et al.  Performance evaluation of score level fusion in multimodal biometric systems , 2010, Pattern Recognit..

[5]  Piero P. Bonissone,et al.  Summarizing and propagating uncertain information with triangular norms , 1990, Int. J. Approx. Reason..

[6]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[7]  Xiaoli Zhou,et al.  Integrating Face and Gait for Human Recognition at a Distance in Video , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[8]  Madasu Hanmandlu,et al.  Multimodal biometric system built on the new entropy function for feature extraction and the Refined Scores as a classifier , 2015, Expert Syst. Appl..

[9]  David Zhang,et al.  A New Framework for Adaptive Multimodal Biometrics Management , 2010, IEEE Transactions on Information Forensics and Security.

[10]  Madasu Hanmandlu,et al.  Score level fusion of multimodal biometrics using triangular norms , 2011, Pattern Recognit. Lett..

[11]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Slobodan Ribaric,et al.  A biometric identification system based on eigenpalm and eigenfinger features , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Kang Ryoung Park,et al.  Iris recognition based on score level fusion by using SVM , 2007, Pattern Recognit. Lett..

[14]  Jules-Raymond Tapamo,et al.  Integrating Iris and Signature Traits for Personal Authentication Using User-Specific Weighting , 2012, Sensors.

[15]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Anil K. Jain,et al.  Likelihood Ratio-Based Biometric Score Fusion , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Jaihie Kim,et al.  Biometric scores fusion based on total error rate minimization , 2008, Pattern Recognit..

[19]  AL-DAWLA.  MULTIMODAL BIOMETRIC SYSTEM USING FACE AND SIGNATURE : A SCORE LEVEL FUSION APPROACH , 2012 .

[20]  Loris Nanni,et al.  Likelihood ratio based features for a trained biometric score fusion , 2011, Expert Syst. Appl..

[21]  Sankar K. Pal,et al.  Some properties of the exponential entropy , 1992, Inf. Sci..

[22]  Julian Fiérrez,et al.  A Comparative Evaluation of Fusion Strategies for Multimodal Biometric Verification , 2003, AVBPA.

[23]  S.R. Mahadeva Prasanna,et al.  Multimodal biometric person authentication system using speech and signature features , 2008, TENCON 2008 - 2008 IEEE Region 10 Conference.

[24]  John K. Tomfohr,et al.  Lecture Notes on Physics , 1879, Nature.

[25]  M. Lara,et al.  Study of Different Fusion Techniques for Multimodal Biometric Authentication , 2008, 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications.

[26]  S. Weber.  A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms , 1983 .

[27]  John C. Kelly,et al.  GEC-based multi-biometric fusion , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[28]  Raafat S. Elfouly,et al.  Biometric Fusion Using Enhanced SVM Classification , 2008, 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[29]  Abubakr Gafar Abdalla,et al.  Probability Theory , 2017, Encyclopedia of GIS.

[30]  Karbhari V. Kale,et al.  MULTIMODAL BIOMETRIC SYSTEM USING FACE AND SIGNATURE: A SCORE LEVEL FUSION APPROACH , 2012 .

[31]  Madasu Hanmandlu,et al.  Content-based Image Retrieval by Information Theoretic Measure , 2011 .

[32]  Shaogang Gong,et al.  Audio- and Video-based Biometric Person Authentication , 1997, Lecture Notes in Computer Science.

[33]  David G. Stork,et al.  Pattern Classification , 1973 .