Robust multi-view face detection using error correcting output codes

  • Authors:

  • Hongming Zhang;Wen Gao;Xilin Chen;Shiguang Shan;Debin Zhao

  • Affiliations:

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

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
  • Year:
  • 2006

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Abstract

This paper presents a novel method to solve multi-view face detection problem by Error Correcting Output Codes (ECOC). The motivation is that face patterns can be divided into separated classes across views, and ECOC multi-class method can improve the robustness of multi-view face detection compared with the view-based methods because of its inherent error-tolerant ability. One key issue with ECOC-based multi-class classifier is how to construct effective binary classifiers. Besides applying ECOC to multi-view face detection, this paper emphasizes on designing efficient binary classifiers by learning informative features through minimizing the error rate of the ensemble ECOC multi-class classifier. Aiming at designing efficient binary classifiers, we employ spatial histograms as the representation, which provide an over-complete set of optional features that can be efficiently computed from the original images. In addition, the binary classifier is constructed as a coarse to fine procedure using fast histogram matching followed by accurate Support Vector Machine (SVM). The experimental results show that the proposed method is robust to multi-view faces, and achieves performance comparable to that of state-of-the-art approaches to multi-view face detection.