Face Recognition using Hidden Markov Eigenface Models

This paper proposes hidden Markov eigenface models (HMEMs) in which the eigenfaces are integrated into separable lattice hidden Markov models (SL-HMMs). SL-HMMs have been proposed for modeling multi-dimensional data, e.g., images, image sequences, 3-D objects. In its application to face recognition, SL-HMMs can perform an elastic image matching in both horizontal and vertical directions. However, SL-HMMs still have a limitation that the observations are assumed to be generated independently from corresponding states; it is insufficient to represent variations in face images, e.g., lighting conditions, facial expressions, etc. To overcome this problem, the structure of probabilistic principal component analysis (PPCA) and factor analysis (FA) is used as a probabilistic representation of eigenfaces. The proposed model has good properties of both PPCA/FA and SL-HMMs: a linear feature extraction and invariances to size and location of images. In face recognition experiments on the XM2VTS database, the proposed model improved the performance significantly.