Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures

This study examines the role of Eigenvector selection and Eigenspace distance measures on PCA-based face recognition systems. In particular, it builds on earlier results from the FERET face recognition evaluation studies, which created a large face database (1,196 subjects) and a baseline face recognition system for comparative evaluations. This study looks at using a combinations of traditional distance measures (City-block, Euclidean, Angle, Mahalanobis) in Eigenspace to improve performance in the matching stage of face recognition. A statistically significant improvement is observed for the Mahalanobis distance alone when compared to the other three alone. However, no combinations of these measures appear to perform better than Mahalanobis alone. This study also examines questions of how many Eigenvectors to select and according to what ordering criterion. It compares variations in performance due-to different distance measures and numbers of Eigenvectors. Ordering Eigenvectors according to a like-image difference value rather than their Eigenvalues is also considered.