Face recognition by elastic bunch graph matching
Intelligent biometric techniques in fingerprint and face recognition
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
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In this paper, we propose a cascaded face-identification framework for enhanced recognition performance. During each stage, the classification is dynamically optimized to discriminate a set of promising candidates selected from the previous stage, thereby incrementally increasing the overall discriminating performance. To ensure improved performance, the base classifier at each stage should satisfy two key properties: (1) adaptivity to specific populations, and (2) high training and identification efficiency such that dynamic training can be performed for each test case. To this end, we adopt a base classifier with (1) dynamic person-specific feature selection, and (2) voting of an ensemble of simple classifiers based on selected features. Our experiments show that the cascaded framework effectively improves the face recognition rate by up to 5% compared to a single stage algorithm, and it is 2-3% better than established well-known face recognition algorithms.