Robust Eye Localization on Multi-View Face in Complex Background Based on SVM Algorithm

Abstract

Focused on multi-view eye localization in complex background, a new detection method based improved SVM is proposed. First, the face is located by AdaBoost detector and the eye searching range on the face is determined. Then, the crossing detection method, which uses the feature of eye and brow integrated as a whole, and the improved SVM detectors trained by large scale multi-view eye examples are adopted to find the candidate eye regions. Based on the fact that the window region with higher weight in SVM classifier is relatively closer to the eye, and the same eye tends to be repeatedly detected by near windows, the candidate eye regions are filtered to refine the eye location on the multi-view face. Experiments show that the method has very good accuracy and robustness to the eye localization with various face post and expression in the complex background.