A markov random field groupwise registration framework for face recognition

Shu Liao, Dinggang Shen, Albert C.S. Chung

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

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 four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. 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.

Original languageEnglish
Article number6571193
Pages (from-to)657-669
Number of pages13
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume36
Issue number4
DOIs
Publication statusPublished - 2014 Apr

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Keywords

  • Face recognition
  • Markov random field
  • anatomical signature
  • correspondences
  • deformable image registration
  • groupwise registration

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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