Face Recognition using Principle Components and Linear Discriminant Analysis

Face recognition has recently received significant attention as one of the challenging and promising fields of computer vision and pattern recognition. It plays a significant role in many security and forensic applications such as person authentication in access control systems and person identification in real time video surveillance systems. This paper studies two appearance-based approaches for feature extraction and dimension reduction, namely, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Numerical experiments were carried out on the ORL face database and many parameters were investigated, this included the effect of changing the number of training images, scaling factor, and the effect of feature vector length on the recognition rate. Classification is performed using the minimum Euclidean distance. The results suggest that the effect of increasing the number of training images has more significance on the recognition rate than changing the image scale. Correlations obtained from numerical experiments on the ORL face database suggest that as the number of training images increases, PCA would yield slightly higher recognition rates. Key-words: face recognition, principal components analysis (PCA), eigenfaces, linear discriminant analysis (LDA), fisherfaces, Euclidean distance.