Color face recognition: A multilinear-PCA approach combined with Hidden Markov Models

Hidden Markov Models (HMMs) have been successfully applied to the face recognition problem. However, existing HMM-based techniques use feature (observation) vectors that are extracted only from the images' luminance component, while it is known that color provides significant information. In contrast to the classical PCA approach, Multilinear PCA (MPCA) seems to be an appropriate scheme for dimensionality reduction and feature extraction from color images, handling the color channels in a natural, “holistic” manner. In this paper, we propose an MPCA-based approach for color face recognition, that exploits the strengths of HMMs as classifiers. The proposed methodology was tested on three publicly available color databases and produced high recognition rates, compared to existing HMM-based methodologies.