Semantics reinforcement and fusion learning for multimedia streams
Proceedings of the 6th ACM international conference on Image and video retrieval
Face recognition with semi-supervised learning and multiple classifiers
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Resampling for face recognition
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A robust and scalable approach to face identification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
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Abstract: We propose to use the AdaBoost algorithm for face recognition. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face recognition, the original Adaboost which uses all given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for classification. The AdaBoost is typically a classification between two classes. To solve the multi-class recognition problem, a majority voting (MV) strategy can be used to combine all the pairwise classification results. However, the number of pairwise comparisons n(n - 1)=2 is huge, when the number of individuals n is very large in the face database. We propose to use a constrained majority voting (CMV) strategy to largely reduce the number of pairwise comparisons, without losing the recognition accuracy. Experimental results on a large face database of 1079 faces of 137 individuals show the feasibility of our approach for fast face recognition.