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.
Index Terms—hidden Markov model, confidence measure, discriminative feature extraction, discriminative training, classification, face recognition.
Swati Raut is with Bharati Vidyapeeth university, College of engineering, Maharashtra, India (e-mail:getdiya2008@gmail.com).
S. H. Patil is with Department of Computer Engineering, Bharathi Vidyapeeth University, College of engineering , Maharashtra, India (e-mail: shpatil@bvucoep.edu.in).
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Cite: Swati Raut and S. H. Patil, "A Review of Maximum Confidence Hidden Markov Models in Face Recognition,"
International Journal of Computer Theory and Engineering vol. 4, no. 1, pp. 119-126, 2012.
