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
Face Recognition Using Angular LDA and SVM Ensembles
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Validated Method for Dense Non-rigid 3D Face Registration
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
Assessment of time dependency in face recognition: an initial study
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Challenges and Research Directions for Adaptive Biometric Recognition Systems
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Adult face recognition in score-age-quality classification space
BioID'11 Proceedings of the COST 2101 European conference on Biometrics and ID management
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Underlying biometrics are biological tissues that evolve over time. Hence, biometric authentication (and recognition in general) is a dynamic pattern recognition problem. We propose a novel method to track this change for each user, as well as over the whole population of users, given only the system match scores. Estimating this change is challenging because of the paucity of the data, especially the genuine user scores. We overcome this problem by imposing the constraints that the user-specific class-conditional scores take on a particular distribution (Gaussian in our case) and that it is continuous in time. As a result, we can estimate the performance to an arbitrary time precision. Our method compares favorably with the conventional empirically based approach which utilizes a sliding window, and as a result suffers from the dilemma between precision in performance and the time resolution, i.e., higher performance precision entails lower time resolution and vice-versa. Our findings applied to 3D face verification suggest that the overall system performance, i.e., over the whole population of observed users, improves with use initially but then gradually degrades over time. However, the performance of individual users varies dramatically. Indeed, a minority of users actually improve in performance over time. While performance trend is dependent on both the template and the person, our findings on 3D face verification suggest that the person dependency is a much stronger component. This suggests that strategies to reduce performance degradation, e.g., updating a biometric template/model, should be person-dependent.