Face Recognition Using an Embedded

Hidden Markov Models (HMM) have been successfully used for speech and action recognition where the data that is to be modeled is one-dimensional. Although attempts to use these one-dimensional HMMs for face recognition have been moderately successful, images are two-dimensional (2-D). Since 2-D HMM's are too complex for real-time face recognition, in this paper we present a new approach for face recognition using an embedded HMM and compare this new approach to the eigenface method for face recognition, and to other HMM-based methods. Speciically, an embedded HMM has equal or better performance than previous methods, with reduced computational complexity.