Abstract
The work presented in this paper focuses on the use of Hidden markov models for face recognition. New discriminative training creation to assure model compactness and discriminability.hidden markov model(HMM) is statistical model in which the system being modeled is assumed to be markov processes with unoabserd state. Hmm can be considered as a simplest dynamics Bayesian network. In Hidden Marko model, the state is not directly visible but output dependent on state is visible. Accordingly w develop the maximum confidence hidden markov modeling (MC-HMM) for face recognition. In MC-HMM we merge transformation matrix to extract discriminative facial features. MC-HMM achieves higher recognition with lower feature dimensions.
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