Singular value decomposition-based approach for face recognition

A method to extract algebraic features of a face image based on the Fourier transform and Singular Value Decomposition (SVD) is introduced, then the method with the algebraic feature is proposed to recognize faces. First, face images are processed by a 2D Fourier transform that has some effective properties such as a linear transform, and is invariant against spatial translation. The amplitudes of the transform coefficients are used to represent the image in the frequency domain. Second, the amplitude representation of the face image is projected onto the two compressed orthogonal matrixes, which come from the SVD of the standard face image obtained by averaging all training samples and then the projecting coefficients are used as the algebraic feature of the face image. The robustness of this feature is proved and used for face recognition. In the matching stage, the traditional Nearest Neighbor Classifier (NNC) is improved to recognize the unknown faces by using Euclidean distance as the similarity measurement. Finally, the standard face database from Olivetti Research Laboratory (ORL) is selected to evaluate the recognition accuracy of the proposed face recognition algorithm. This database includes face images with different expressions, small occlusions, different illumination conditions and different poses, etc. The recognition accuracy is up to 100.00% by selecting appropriate values of the parameters. The effectiveness of the proposed face recognition algorithm and its insensitivity to the facial expression, illumination and posture are shown in terms of both the absolute performance indices and the comparative performance against some popular face recognition schemes such as Singular Value decomposition-based method.