Algorithm Study For Face Recognition Using Hidden Markov Models

The use of hidden Markov models(HMM) for faces is motivated by their partial invariance to variations in scaling and by the structure of faces. The most significant facial features of a frontal face include the hair, forehead, eyes, nose and mouth. These features occur in a natural order, from top to bottom, even if the images undergo small rotations in the image plane. Therefore, the image of a face may be modeled using a one-dimensional HMM by assigning each of these regions to a state. The observation vectors are obtained from the DCT coefficients.The HMM were tested for face recognition and detection. Compared to the other methods, this system offers a more flexible framework for face recognition and detection.