关键词: 人脸检测, 全卷积网络, 联合回归

Abstract: Aiming at the problem of low face detection accuracy caused by changes in lighting, low resolution, posture and expression, and the generalization of algorithms caused by most face detection algorithms using a single convolutional neural network to extract features, a three-layer convolutional neural network structure consisting of shallow and deep cascade is proposed. The face candidate region is quickly located by the full convolutional neural network. Then the depth neural network is used to extract the face robustness feature, and the candidate region is further classified and verified. The joint regression face method is used to determine the final face position and improve the detection accuracy. At the same time, the commonly used non-maximum value suppression method is improved by weighting the reduction score, and the missed detection problem caused by the overlapping of adjacent faces is solved. The experimental results show that the model is robust to the above-mentioned factors that cause low face detection accuracy, and it has high accuracy and running speed in FDDB dataset. The improved non-maximum suppression algorithm also has a certain improvement on the test accuracy of FDDB.

Key words: face detection, full convolutional network, joint regression