In this paper we present an approach for face recognition based on Hidden Markov Model (HMM) in
compressed domain. Each individual is regarded as an HMM which consists of several face images. A
set of DCT coefficients as observation vectors obtained from original images by a window are clustered
by K-means method using to be the feature of face images. These classified features are applied to train
HMMs, so as to get the parameters of systems. Based on the proposed method, both Yale face database
and ORL face database are tested. Compared to the other methods relevant to HMM methods reported
so far on the two face databases, experimental results by proposed method have shown a better
recognition rate and lower computational complexity cost.