Procrustes Analysis and Moore-Penrose Inverse Based Classifiers for Face Recognition

摘要

We propose two new classifiers, one based on the classical Procrustes analysis and the other on the Moore-Penrose inverse in the context of face recognition. The Procrustes based classifier has recognition rates of 97.5%, 96.19%, 71.40% and 96.22% for the ORL, YALE, GIT and the FERET database respectively. The Moore-Penrose classifier has comparative recognition rates of 98%, 99.04%, 87.40% and 96.22% for the same databases. In addition to these classifiers, we also propose new parameters that are useful for comparing classifiers based on their discriminatory power and not just on their recognition rates. We also compare the performance of our classifiers with the baseline PCA and LDA techniques as well as the recently proposed discriminative common vectors technique for the above face databases.