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.


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    Title :

    Robust Multi-view Face Detection Using Error Correcting Output Codes


    Contributors:
    Zhang, Hongming (author) / Gao, Wen (author) / Chen, Xilin (author) / Shan, Shiguang (author) / Zhao, Debin (author)


    Publication date :

    2006-01-01


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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