A Novel Face Features Extraction Method Based on DCT and KDA

A novel face feature extraction method is presented in this paper. In this method, the raw face images are denoised by DCT, and dimension reduced features are obtained, then the KDA is performed on the feature vectors to enhance discriminant power. Finally, the NN classifier is selected to perform face classification. The experimental results on ORL face database show that the proposed method achieves an average recognition accuracy of 97.3% using only 28 features and the ‘leave one out’ recognition rate is 99.5%. Moreover, the dicriminant power is enhanced effectively, and the computing complexity and feature dimensions are reduced greatly.