Numerous method have been developed for holistic face recognition with impressive performance. It has become one of the most challenging tasks in Biometrics. Among different biometric traits, face and palm print recognition receive great amount of attention in the past decade. They can get high recognition rate. Feature representation and classification are two key steps for face recognition. This paper deals with a face recognition method using Distance classifier based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). A novel method for face recognition was presented based on combination of PCA& LDA. The Principal Component Analysis was used for feature extraction and dimension reduction. Linear Discriminate Analysis was used to further improve the separability of samples in the subspace and extract LDA features. The normalization had been done to eliminate redundant information interference previous to feature extraction. The experiments were implemented by using ORL face database. Comparing PCA, LDA and Distance Classifier, our approach is to improve the face recognition rate.
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