A New Ergodic HMM-Based Face Recognition Using DWT and Half of the Face

Dealing with disguises, illumination and expression variations are important and challenging problems in the face recognition area. Considering the axis-symmetrical structure of the face, we propose a face recognition algorithm using the ergodic Hidden Markov Model (HMM) as a classifier and the image of half of the face; while the Discrete Wavelet Transform (DWT) is applied to generate observation vectors. We evaluate the proposed method on AR, Yale and Faces94 face datasets. The results represent the superiority of our method, compared with some other state-of-the-art methods, in terms of recognition rates, computational complexity, and memory consumption.