Recently, tremendous strides have been made in generic object detection when used to detect faces, and there are still some remaining challenges. In this paper, a novel method is proposed named multilevel single stage network for face detection (MSNFD). Three breakthroughs are made in this research. Firstly, multilevel network is introduced into face detection to improve the efficiency of anchoring faces. Secondly, enhanced feature module is adopted to allow more feature information to be collected. Finally, two-stage weight loss function is employed to balance network of different levels. Experimental results on the WIDER FACE and FDDB datasets confirm that MSNFD has competitive accuracy to the mainstream methods, while keeping real-time performance.
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