Boosting for Fast Face Recognition

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.