| 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. |
