Boosting Gabor Feature Classifier for Face Recognition Using Random Subspace

Gabor feature has been widely viewed as a good representation method for face recognition. AdaBoost is an excellent machine learning technique. Learning Gabor feature based classifier using AdaBoost is one of the best face recognition algorithms. However, dimensionality of Gabor feature space usually is very high, which makes the training program need huge memory or else take a very long time to run. In this paper, we propose a method which not only can solve the problem but also can improve recognition accuracy. Several subspaces with moderate size are randomly generated from original high dimensional Gabor feature space. Then strong classifier is trained in every random subspace (T. Kam Ho, 1998) respectively and the outputs of multiple classifiers are combined in the final decision. Experimental results demonstrate that the method saves a great amount of training time, and achieves an exciting recognition rate of 97.91% on the FERET Fb test set