A comparison of discrete and continuous output modeling techniques for a pseudo-2D hidden Markov model face recognition system

Face recognition has become an important topic within the field of pattern recognition and computer vision. In this field a number of different approaches to feature extraction, modeling and classification techniques have been tested. However, many questions concerning the optimal modeling techniques for high performance face recognition are still open. The face recognition system developed by our research group uses a discrete cosine transform (DCT) combined with the use of pseudo-2D hidden Markov models (P2DHMM). In the past our system used continuous probability density functions and was tested on a smaller database. This paper addresses the question of the presence of a major difference in recognition performance with discrete production probabilities compared to continuous ones. Therefore the system is tested using a larger subset of the FERET database. We show that we are able to achieve higher recognition scores and an improvement concerning the computation speed by using discrete modeling techniques