Face Recognition Based on Features by PCA/ICA and Classification with SVM

This paper deals with combined approach of principle component analysis （PCA） and independent component analysis （ICA） to get the representation basis of face image set and proposes a new multi-step approach to extend support vector machine （SVM） capability to deal with multi-class face recognition by incorporating with the elimination strategy. Based on the one-against-one classifying strategy,it sorts the discrimination functions according to their own Vapnik-Chervonenkis confidence and uses the redundancy among them to decrease the discrimination error in case of rejecting decision. Experiments with two face-databases show that the proposed method has reached a higher recognition rate with reasonable time cost.