Bayesian Face Recognition Based on Markov Random Field Modeling

In this paper, a Bayesian method for face recognition is proposed based on Markov Random Fields (MRF) modeling. Constraints on image features as well as contextual relationships between them are explored and encoded into a cost function derived based on a statistical model of MRF. Gabor wavelet coefficients are used as the base features, and relationships between Gabor features at different pixel locations are used to provide higher order contextual constraints. The posterior probability of matching configuration is derived based on MRF modeling. Local search and discriminate analysis are used to evaluate local matches, and a contextual constraint is applied to evaluate mutual matches between local matches. The proposed MRF method provides a new perspective for modeling the face recognition problem. Experiments demonstrate promising results. Key words: Face Recognition, Markov random field, learning.

• Type: Conference
• Year: 2009
• Where: ICB
• Authors: Rui Wang, Zhen Lei, Meng Ao, Stan Z. Li
• Keywords: Biometrics, Contextual Constraint, Face Recognition, ICB 2009, Markov Random Fields