Face detection using one-class-based support vectors

Almost all the proposed approaches regard face detection as a typical two-class pattern classification task, i.e., face pattern vs. non-face pattern, and learn face detector from face samples and non-face samples. In practice, face pattern model can be established easily, while it is hard to gain a perfect non-face pattern model, for any pattern beyond face pattern (cat, plane, flower etc.) should belong to non-face pattern. In this paper, we propose a novel face detection approach based on one-class SVM (OCSVM), in which face detection is just considered to be a one-class pattern problem. Support vectors are used to model face pattern, and non-face patches in given images are rejected based on this model. In order to further improve performance, course-to-fine strategy is used in both the training and detection procedure. Extensive experiments show that the proposed method has an encouraging performance.