Shu Liao , The Hong Kong University of Science and Technology, Hong Kong
Dinggang Shen , The University of North Carolina at Chapel Hill, Chapel Hill
Albert C. S. Chung , The Hong Kong University of Science and Technology, Hong Kong
In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on three publicly available databases: FERET, CAS-PEAL-R1, and FRGC ver 2.0. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.
Computer Vision, Face Recognition
S. Liao, D. Shen and A. C. Chung, "A Markov Random Field Groupwise Registration Framework for Face Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence.