Face recognition under occlusions and variant expressions with partial similarity

  • Authors:
  • Xiaoyang Tan;Songcan Chen;Zhi-Hua Zhou;Jun Liu

  • Affiliations:
  • Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Biodesign Institute, Arizona State University, Tempe, AZ and Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2009

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Abstract

Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. In particular, few researchers have addressed the challenge to recognize noncooperative or even uncooperative subjects who try to cheat the recognition system by deliberately changing their facial appearance through such tricks as variant expressions or disguise (e.g., by partial occlusions). This paper addresses these problems within the framework of similarity matching. A novel perceptionin-spired nonmetric partial similarity measure is introduced, which is potentially useful in dealing with the concerned problems because it can help capture the prominent partial similarities that are dominant in human perception. Two methods, based on the general golden section rule and the maximum margin criterion, respectively, are proposed to automatically set the similarity threshold. The effectiveness of the proposed method in handling large expressions, partial occlusions, and other distortions is demonstrated on several well-known face databases.