Face Recognition Based on Subject Dependent Hidden Markov Models

In this paper we present an automatic face recognition system based on incremental Singular Values Decomposition (SVD) and subject dependent Hidden Markov Models (HMM). For each subject, an individual HMM is trained with features, extracted from the orthogonal decomposition (SVD) of the subject's training images. The main advantage of the proposed SVD-HMM recognition system is the robustness against image dimensionality reduction. The system was tested on two benchmark face datasets - the Olivetti Research Laboratory (ORL) and the YALE database. The SVD-HMM was further compared with a standard SVD face recognition. SVD applied to the original (full size) images performs similarly to the SVD-HMM applied to the compressed (half of the original size) images. SVD degrades rapidly when the image is compressed.