Matrix computations (3rd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Inverse Approach to Adaptive Multiclass Pattern Classification
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Discrete choice models for static facial expression recognition
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
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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.