A robust face detector under partial occlusion
K. Hotta · 2005
This paper presents a robust face detector under partial occlusion. In recent years, the effectiveness of support vector machines (SVM) to object detection has been reported. However, conventional methods apply one kernel to global features. Therefore, those methods are not robust to occlusion because global features are influenced easily by noise or occlusion. To overcome this problem, SVM with local kernels is proposed. It is used to realize a robust face detector under partial occlusion. The robustness of the proposed method under partial occlusion is shown by using occluded face images. The proposed method can detect faces wearing sunglasses or a scarf. It is also confirmed that the proposed method is superior to the conventional SVM with global kernel.