Improved HMM based face recognition system
نویسنده
چکیده
In this paper we present a face recognition system based on an embedded hidden Markov models, which uses an efficient set of observation vectors obtained from the 2D-DCT coefficients. Two dimensional data such as images are much better modeled by a two dimensional HMM compared to a one dimensional HMM, but the computational complexity of the first makes the recognition process difficult. The embedded HMM realizes a compromise between the two models: due to its pseudo two dimensional structure is able to model the two dimensional data better than the one dimensional HMM and is computationally less complex than the two dimensional HMM. In order to improve the robustness of the recognition system to different illumination we apply an illumination normalization technique (CLAHE) prior to analysis.
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