Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Bayesian face recognition using deformable intensity surfaces
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Journal of Cognitive Neuroscience
Turning pervasive computing into mediated spaces
IBM Systems Journal
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face recognition with local steerable phase feature
Pattern Recognition Letters
Face recognition with local steerable phase feature
Pattern Recognition Letters
Efficient artificial hippocampus algorithm for biometric authentication system
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
An Intensity and Size Invariant Real Time Face Recognition Approach
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Recent advances in face biometrics with Gabor wavelets: A review
Pattern Recognition Letters
Performance analysis of subspace LDA approach for face recognition
TELE-INFO'10 Proceedings of the 9th WSEAS international conference on Telecommunications and informatics
System design and assessment methodology for face recognition algorithms
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
| Hi-index | 0.00 |
Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real world. In this paper we (1) present a general verification protocol and apply it to the results from the Sep96 FERET test, and (2) discuss and present results on the effects of database size and variability on identification and verification performance.