Fast side-face interference resistant face detection method

Abstract

Disclosed is a fast side-face interference resistant face detection method, in which a user selects an ordinary image, uses a deep neural network to extract image features, and then determines an exact location of a face. A training method for face detection uses a pure data-driven manner, uses an ordinary face image and a face boundary box as inputs, uses mirror symmetry and Gaussian filtering to perform data augmentation, and uses migration learning and hard example mining to enhance training effects. After a face image is read, the image is firstly scaled, and then placed into the deep neural network to extract features, and generate a plurality of face likelihood boxes and confidence scores of the face likelihood boxes, and finally the most appropriate face likelihood box is selected in a non-maximum suppression manner. No specific requirements are set on an angle of the face image, and a detection effect of a side face is still very obvious. In addition, the detection method above is simple, employs an end-to-end detection manner, and can be applied to a real-time environment.