Comparison of combination methods utilizing T-normalization and second best score model

The combination of biometric matching scores can be enhanced by taking into account the matching scores related to all enrolled persons in addition to traditional combinations utilizing only matching scores related to a single person. Identification models take into account the dependence between matching scores assigned to different persons and can be used for such enhancement. In this paper we compare the use of two such models - T-normalization and second best score model. The comparison is performed using two combination algorithms - likelihood ratio and multilayer perceptron. The results show, that while second best score model delivers better performance improvement than T-normalization, two models are complementary to each other and can be used together for further improvements.

[1]  HoTin Kam.  The Random Subspace Method for Constructing Decision Forests , 1998 .

[2]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  J.P. Campbell,et al.  Allowing good impostors to test , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[4]  Roland Auckenthaler,et al.  Score Normalization for Text-Independent Speaker Verification Systems , 2000, Digit. Signal Process..

[5]  Horst Bunke,et al.  Using HMM based recognizers for writer identification and verification , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[6]  Nalini K. Ratha,et al.  Biometric Verification: Looking Beyond Raw Similarity Scores , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[7]  Julian Fiérrez,et al.  Bayesian adaptation for user-dependent multimodal biometric authentication , 2005, Pattern Recognit..

[8]  Jirí Navrátil,et al.  The awe and mystery of t-norm , 2003, INTERSPEECH.

[9]  S. Tulyakov,et al.  Identification Model for Classifier Combinations , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.

[10]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Venu Govindaraju,et al.  Classifier Combination Types for Biometric Applications , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[12]  Anil K. Jain,et al.  Decision-Level Fusion in Fingerprint Verification , 2001, Multiple Classifier Systems.

[13]  Aaron E. Rosenberg,et al.  Speaker background models for connected digit password speaker verification , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[14]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Samy Bengio,et al.  A unified framework for score normalization techniques applied to text-independent speaker verification , 2005, IEEE Signal Processing Letters.

[16]  Qiang Ji,et al.  Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Arun Ross,et al.  Learning user-specific parameters in a multibiometric system , 2002, Proceedings. International Conference on Image Processing.