AN EIGENFACE ALGORITHM IN HUMAN FACE RECOGNITION SYSTEM

Taking image as matrix, an eigenface algorithm uses eigenvalues and corresponding eigenvectors in recognition. The algorithm has advantage of no need of extracting geometric features of eyes, noses and mouths, but doesn’t reach high recognition rate when single sample image per person is used for training. Another problem is that the larger the number of face modes is, the more complex the computation becomes. In this paper, an algorithm is proposed taking multi samples as sub modes and grouped face modes into small intersection ones to reduce computation and gain system extension property. In combination, the sum rule based on Bayesian theory is used. The face recognition experiment with the ORL and AR face databases shows that eigenface algorithm using multi samples has reached a high recognition rate and a reasonable time cost. Applying distributed computation, the recognition system could be trained by grouped face modes and has the convenience of extension when new face modes are to be added.