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A face recognition system using different local features with different distance measures is proposed in this paper. Proposed method is fast and gives accurate detection. Feature vector is based on Eigen values, Eigen vectors, and diagonal vectors of sub images. Images are partitioned into sub images to detect local features. Sub partitions are rearranged into vertically and horizontally matrices. Eigen values, Eigenvector and diagonal vectors are computed for these matrices. Global feature vector is generated for face recognition. Experiments are performed on benchmark face YALE database. Results indicate that theproposed method gives better recognition performance in terms of average recognized rate and retrieval time compared to the existing methods.

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In this paper we present a hybrid approach for efficient human face recognition. The proposed method is based on linear discriminant analysis of image in DCT domain with a combination of details of DWT. And the similarity measure Minkowshi is used here. This approach reduces the storage requirement and computation time while preserving the data. The approach LDA -DCT-hybrid DWT is evaluated on Matlab using ORL face database. Compared to previous methods the proposed method improves feature extraction and retrieval rate.

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