Robust Multi-view Face Detection Using Error Correcting Output Codes

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 overcomplete 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.