Appearance-based face recognition techniques are appropriate for reducing the volume of computation for fast image analysis and classification. Face recognition plays a significant role in many security and forensic applications including person authentication for access control systems and person identification in real time video surveillance systems. This paper examines two appearance-based approaches for feature extraction and dimension reduction, namely, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Numerical experiments were conducted on the ORL face database to investigate the effect of changing the number of training images, scaling factor, and the effect of feature vector length on the recognition rate. 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 datab!
ase suggest that as the number of training images increases, PCA would yield slightly higher recognition rates.