Face Detection Based on Two Dimensional Principal Component Analysis and Support Vector Machine

An efficient method of face detection based on two-dimensional principal component analysis (PCA) incorporating with support vector machine (SVM) is proposed in this paper. Firstly, a 2DPCA coarse filter with relatively lower computational complexity is applied to the whole input image to filter out most of the non-face, then follows the SVM classifier to make the final decision, so the detection process is speeded up. As opposed to PCA, 2DPCA is based on 2D image matrices rather than ID vector so the image matrix does not need to be transformed into a vector prior to feature extraction. The experiment results show that the method can effectively detect faces under complicated background, and the processing time is shorter than using SVM alone