Algorithm to estimate biometric performance change over time

The authors present an algorithm that models the rate of change of biometric performance over time on a subject-dependent basis. It is called ‘homomorphic users grouping algorithm’. Although the model is based on very simplistic assumptions that are inherent in linear regression, it has been applied successfully to estimate the performance of talking face and speech identity verification modalities, as well as their fusion, over a period of more than 600 days. Their experiments carried out on the MOBIO database show that subjects exhibit very different performance trends. Although the performance of some users degrades over time, which is consistent with the literature, they also found that for a similar proportion of users, their performance actually improves with use. The latter finding has never been reported in the literature. Hence, their findings suggest that the problem of biometric performance degradation may be not as serious as previously thought, and so far, the community has ignored the possibility of improved biometric performance over time. The findings also suggest that adaptive biometric systems, that is, systems that attempt to update biometric templates, should be subject-dependent.

[1]  J. Kittler,et al.  Incorporating Variation of Model-specific Score Distribution in Speaker Verification Systems , 2007 .

[2]  Patrick J. Flynn,et al.  Experimental Evaluation of Eye Location Accuracies and Time-Lapse Effects on Face Recognition Systems , 2005, AVBPA.

[3]  Matti Pietikäinen,et al.  Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[4]  Douglas A. Reynolds,et al.  Speaker Verification Using Adapted Gaussian Mixture Models , 2000, Digit. Signal Process..

[5]  Samy Bengio,et al.  Why do multi-stream, multi-band and multi-modal approaches work on biometric user authentication tasks? , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Bruce A. Draper,et al.  Biometric zoos: Theory and experimental evidence , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[7]  J. Pierrot.  Elaboration et validation d'approches en verification du locuteur , 1998 .

[8]  Patrick J. Flynn,et al.  Assessment of Time Dependency in Face Recognition: An Initial Study , 2003, AVBPA.

[9]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[10]  Josef Kittler,et al.  On using error bounds to optimize cost-sensitive multimodal biometric authentication , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Dominique Genoud,et al.  Reconnaissance et transformation de locuteurs , 1999 .

[12]  Josef Kittler,et al.  Incorporating Model-Specific Score Distribution in Speaker Verification Systems , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[13]  J. Kittler,et al.  A methodology for separating sheep from goats for controlled enrollment and multimodal fusion , 2008, 2008 Biometrics Symposium.

[14]  Neil Yager,et al.  Worms, Chameleons, Phantoms and Doves: New Additions to the Biometric Menagerie , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

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

[16]  Douglas A. Reynolds,et al.  SHEEP, GOATS, LAMBS and WOLVES A Statistical Analysis of Speaker Performance in the NIST 1998 Speaker Recognition Evaluation , 1998 .

[17]  Hideki Imai,et al.  Wolf Attack Probability: A Theoretical Security Measure in Biometric Authentication Systems , 2008, IEICE Trans. Inf. Syst..

[18]  Elham Tabassi,et al.  Performance of Biometric Quality Measures , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Josef Kittler,et al.  On the Use of Log-Likelihood Ratio Based Model-Specific Score Normalisation in Biometric Authentication , 2007, ICB.

[20]  Ke Chen,et al.  Towards better making a decision in speaker verification , 2003, Pattern Recognit..

[21]  Josef Kittler,et al.  A Method for Estimating Authentication Performance over Time, with Applications to Face Biometrics , 2007, CIARP.

[22]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[23]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[24]  Samy Bengio,et al.  F-ratio client dependent normalisation for biometric authentication tasks , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[25]  John Daugman,et al.  Biometric decision landscapes , 2000 .

[26]  Samy Bengio,et al.  Database, protocols and tools for evaluating score-level fusion algorithms in biometric authentication , 2006, Pattern Recognit..

[27]  Gian Luca Marcialis,et al.  An Experimental Analysis of the Relationship between Biometric Template Update and the Doddington's Zoo: A Case Study in Face Verification , 2009, ICIAP.

[28]  Patrick J. Flynn,et al.  Empirical Studies of the Existence of the Biometric Menagerie in the FRGC 2.0 Color Image Corpus , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[29]  Samy Bengio,et al.  Client Dependent GMM-SVM Models for Speaker Verification , 2003, ICANN.

[30]  John Daugman,et al.  New Methods in Iris Recognition , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Sébastien Marcel,et al.  Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS , 2003, AVBPA.

[32]  S. Furui,et al.  Cepstral analysis technique for automatic speaker verification , 1981 .

[33]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[34]  Craig I. Watson,et al.  The myth of goats :: how many people have fingerprints that are hard to match? , 2005 .

[35]  Anil K. Jain,et al.  Validating a Biometric Authentication System: Sample Size Requirements , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Josef Kittler,et al.  A multimodal biometric test bed for quality-dependent, cost-sensitive and client-specific score-level fusion algorithms , 2010, Pattern Recognit..

[37]  Albert Ali Salah,et al.  Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms , 2009, IEEE Transactions on Information Forensics and Security.

[38]  Javier Hernando,et al.  On the use of score pruning in speaker verification for speaker dependent threshold estimation , 2004, Odyssey.