Face Recognition Using Different Local Features With Different Distance Techniques
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, Eigenvectors, 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 the proposed method gives better recognition performance in terms of average recognized rate and retrieval time compared to the existing methods.