Face and Head Detection for Back View Cases Using Gradient Features and Learning Architecture

Face detection is an important topic in computer vision. Although there were numerous face detection algorithms over the past decade, some challenging cases remain unresolved. In this paper, we focus on the images with back-view heads. First, features including the image gradient and chrominance are applied to selecting regions which may be hairs. Next, redundant bounding boxes are filtered out by a CNN-based regression model and non-maximum suppression. Finally, geometric information is used for further filtering. Simulations show that the proposed method improves the detection performance for back-view heads, which are hard to be addressed by state-of-the-art face detection methods.