Face Detection Using R-FCN Based Deformable Convolutional Networks
Qiaosong Chen, Shen Fahai, Yuanyuan Ding, Panhao Gong, Ya Xiong Tao, Jin Wang · 2018
The recent years witnessed great improvements in systems of region-based face detection. However, the variations in occlusion, scale, illumination, pose and facial expressions make face detection in the wild still a challenge to be solved. In this paper, a Region-based Fully Convolutional Networks (R-FCN) based deep face detection framework is proposed. Several new techniques are utilized in our framework, including Deformable Convolutional Networks (DCN), Feature Pyramid Networks (FPN) and Focal Loss. Experiment results on three common challenging face detection benchmarks, FDDB, AFW and WIDER FACE, show the proposed approach is robust and performance outperforms most of previous methods, especially for addressing heavy occlusion, part deformation and complex perspective.