Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution

  • Authors:

  • Shiguang Shan;Yizheng Chang;Wen Gao;Bo Cao;Peng Yang

  • Affiliations:

  • Institute of Computing Technology Chinese Academy of Sciences;Department of Computer Science, Harbin Institute of Technology;Institute of Computing Technology Chinese Academy of Sciences and Department of Computer Science, Harbin Institute of Technology;Institute of Computing Technology Chinese Academy of Sciences;Institute of Computing Technology Chinese Academy of Sciences

  • Venue:
  • FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
  • Year:
  • 2004

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Abstract

In this paper, we present the rarely concerned curse of mis-alignment problem in face recognition, and propose a novel mis-alignment learning solution. Misalignment problem is firstly empirically investigated through systematically evaluating Fisherface's sensitivity to misalignment on the FERET face database by perturbing the eye coordinates, which reveals that the imprecise localization of the facial landmarks abruptly degenerates the Fisherface system. We explicitly define this problem as curse of mis-alignment to highlight its graveness. We then analyze the sources of curse of mis-alignment and group the possible solutions into three categories: invariant features, mis-alignment modeling, and alignment retuning. And then we propose a set of measurement combining the recognition rate with the alignment error distribution to evaluate the overall performance of specific face recognition approach with its robustness against the misalignment considered. Finally, a novel misalignment learning method, named E-Fisherface, is proposed to reinforce the recognizer to model the misalignment variations. Experimental results have impressively indicated the effectiveness of the proposed E-Fisherface in tackling the curse of mis-alignment problem.