Abstract: In order to improve the average accuracy of face detection in railway face brushing and ticket checking, this paper proposed a face multi-attribute detection algorithm based on RetinaFace by studying and analyzing the face detection algorithm retinaface and formulating the loss function according to the application scene of the gate. It was implemented the accurate output of the information such as the face frame position, whether the face was wearing sunglasses, and the degree of occlusion. The algorithm used the lightweight backbone network MobileNet-0.25 network structure, and removed unnecessary branches to reduce the computational cost. The detection rate of the algorithm on the railway standard face occlusion data set reached 95.4%, and the recognition accuracy of different occlusion degrees reached 99.2%.