Multi-Module Singular Value Decomposition for Face Recognition

The paper introduces a face recognition method using probabilistic subspaces analysis on multi-module singular value features of face images. Singular value vector of a face image is valid feature for identification. But the recognition rate is low when only one module singular value vector is used for face recognition. To improve the recognition rate, many sub-images are obtained when the face image is divided in different ways, with all singular values of each image used as a new sample vector of the face image. These multi-module singular value vectors include all features of a face image from local to the whole, so more discriminator information for face recognition is obtained. Subsequently, probabilistic subspaces analysis is used under these multimodule singular value vectors. The experimental results demonstrate that the method is obviously superior to corresponding algorithms and the recognition rate is respectively 97.5% and 99.5% in ORL and CAS-PEAL-R1human face image databases.