An embedded HMM-based approach for face detection and recognition
نویسندگان
چکیده
In this paper we describe an embedded Hidden Markov Model (HMM)-based approach for face detection and recognition that uses an eecient set of observation vectors obtained from the 2D-DCT coeecients. The embedded HMM can model the two dimensional data better than the one-dimensional HMM and is computationally less complex than the two-dimensional HMM. This model is appropriate for face images since it exploits an important facial characteristic: frontal faces preserve the same structure of \super states" from top to bottom, and also the same left-to-right structure of \states" inside each of these \super states".
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