A novel Markov random field based deformable model for face recognition

In this paper, a new scheme to address the face recognition problem is proposed. Different from traditional face recognition approaches which represent each facial image by a single feature vector as the classification problem, the proposed method establishes a new way to formulate the face recognition problem as a deformable image registration problem. The main contributions of the paper lie in the following aspects: (i) Each pixel is represented by an anatomical feature signature calculated from its corresponding best scale salient region by using a new salient region detector based on the survival exponential entropy (SEE); (ii) The face recognition problem is formulated as a deformable image registration problem, the deformation model is represented by a Markov random field (MRF) labeling framework. Explicit pixel correspondence is established by the deformation framework. (iii) The survival exponential entropy based normalized mutual information (SEE-NMI) is proposed and integrated with the MRF based deformation model as the similarity measure to reflect the similarity between two facial images. The proposed method is evaluated on the FERET and FRGC version 2 databases and compared with several state-of-the-art face recognition approaches. Experimental results show that the proposed method achieves the highest recognition rate among all the compared approaches.