Experimental research on occlusion face detection method based on attention mechanism

Abstract

Abstract Face occlusion is a common problem in real life. Aiming at the problem that the face detection effect is poor when there is occlusion in the natural environment, this paper proposes the RetinaNet occlusion face detection network based on the fusion image enhancement network and the attention mechanism. Traditional RetinaNet, as a typical single-stage detector, does not explicitly give the process of extracting candidate regions and directly obtains the results of object detection. Compared with the two-stage detector, the processing speed is faster, but the accuracy will also be reduced. In this paper, the attention mechanism and image enhancement strategy are introduced to improve the detection accuracy while ensuring the detection speed. The experimental results on the MAFA dataset show that the RetinaNet model based on the attention mechanism proposed in this paper achieves 89.7% accuracy in detecting occluded faces. The feasibility and effectiveness of the model are verified.