A Face Quality Evaluation Method Based on DCNN

In response to the problem of low recognition rate caused by low quality face image in face recognition, a face quality evaluation method based on deep convolutional neural network is proposed. Firstly, the depthwise separable convolution method is adopted to establish a depth-convolution model which contain 8 blocks to achieve face feature extraction. Secondly, the last layer of the convolution module connects 8 output branches, and uses regression and classification methods to predict the probability of 8 attributes, namely yaw, pitch, norm, opening mouth, occlusion, blur, dim, and closed eyes. The proposed method also proves that norm value can help analyze the quality of face image. Finally, the weight optimization is realized by taking the video pass rate as the optimal objective function. the quality score of face image can be calculated by weighting each branch The higher the score, the better the quality of the face image. In this paper, an end-to-end face quality evaluation model is implemented by using depthwise separable convolution method. The parameters of the model are less than 60000, the operation speed is fast, and the evaluation results are accurate; and it can filter out low-quality face image in real time, and recommend high-quality face image to face recognition model.