Abstract: Aiming at the problem that Kernel Principal Component Analysis(KPCA) can not effectively use the feature of face symmetry, and generally lacks of training samples in face recognition, so the recognition rate is low. Therefore, this paper proposes a Symmetrical Kernel Principal Component Analysis(SKPCA) algorithm. This algorithm fully utilizes the face mirror symmetry, the odd symmetry samples and the even symmetry samples are received by mirror transforming for training samples. Odd/even symmetrical principal components are respectively extracted. A nearest neighbor classifier is employed to classify the extracted features. Experimental results show that this algorithm enlarges the number of training samples, when the polynomial order number is 2, the recognition rate of this algorithm is better than that of the KPCA algorithm, and recognition time is shorter than KPCA algorithm.
Support Vector Machine(SVM),
Principal Component Analysis(PCA),
Kernel Principal Component Analysis(KPCA)