earticle

영어

A face recognition (FR) system is automatically identifying or verifying a personal face acquired from a digital camera or a image generation device. In order to do this, facial features from the acquired image should be extracted and compared with a facial database. All FRs face an obstacle related to the viewing angle of the face including poor lighting and low resolution. Because of those problems, its recognition rate substantially decreases. In this paper, a newly weighted regularization parameter based FR system which can improve recognition rate under certain environmental constraints is proposed. This approach is based on the conventional regularized linear discriminant analysis (R-LDA) and includes Artificial Neural Network (ANN) which can improve face recognition rate with a prominent classification ability. The revised R-LDA algorithm is attempted to address the Small Sample Size (SSS) problem that encountered in all FRs and the ANN is useful to detect the frontal views of faces. This algorithm has been tested over 350 images (35 classes) of Olivetti Research Lab (ORL) database using MATLAB. Its test results give us recognition rates of above 95%. In addition, it is also tested on the mirror and combination of the ORL database and the recognition performances are shown that the system is fairly robust and has the performance of more than 90%.