Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Meta-Analysis of Face Recognition Algorithms
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face Recognition Vendor Test 2002
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Face recognition vendor test 2002 performance metrics
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
2D and 3D face recognition: A survey
Pattern Recognition Letters
Impact of Gaze Analysis on the Design of a Caption Production Software
UAHCI '09 Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services
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The performance of a face recognition algorithm is typically characterised by correct identification rate under the closed-world assumption. To be of greatest practical use, the closed-world assumption must be relaxed and the classifier used both for detection and identification. It is put forward that for open-world applications, the false alarm rate of the classifier is at least as important as the identification rate. Under a repeated verification model, all face recognisers exhibit a rapid non-linear increase in false alarm rate with the false alarm rate of the one-to-one verification used. If the one-to-one false alarm rate is not strictly controlled, the overall classifier will be all but unusable. A method is presented to predict the false alarm rate of a large gallery classifier using only a small data set. It is then shown that the false alarm error rate is always greater than the identification error rate. Therefore the false alarm rate is a more difficult criterion to minimise when designing a classifier.