Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Biometrical fingerprint recognition: don't get your fingers burned
Proceedings of the fourth working conference on smart card research and advanced applications on Smart card research and advanced applications
An Analysis of Minutiae Matching Strength
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Biometric Systems: Technology, Design and Performance Evaluation
Biometric Systems: Technology, Design and Performance Evaluation
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
From Scores to Face Templates: A Model-Based Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Biometrics
Comparison of MLP and GMM classifiers for face verification on XM2VTS
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Biometrics: a tool for information security
IEEE Transactions on Information Forensics and Security
Bayesian hill-climbing attack and its application to signature verification
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Acquisition scenario analysis for face recognition at a distance
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
An evaluation of indirect attacks and countermeasures in fingerprint verification systems
Pattern Recognition Letters
A high performance fingerprint liveness detection method based on quality related features
Future Generation Computer Systems
Computer Vision and Image Understanding
Pattern Recognition Letters
A novel hand reconstruction approach and its application to vulnerability assessment
Information Sciences: an International Journal
Hi-index | 0.01 |
In this paper, we use a hill-climbing attack algorithm based on Bayesian adaption to test the vulnerability of two face recognition systems to indirect attacks. The attacking technique uses the scores provided by the matcher to adapt a global distribution computed from an independent set of users, to the local specificities of the client being attacked. The proposed attack is evaluated on an eigenface-based and a parts-based face verification system using the XM2VTS database. Experimental results demonstrate that the hill-climbing algorithm is very efficient and is able to bypass over 85% of the attacked accounts (for both face recognition systems). The security flaws of the analyzed systems are pointed out and possible countermeasures to avoid them are also proposed.