Face Detection Based on Dynamic Min Size and Dense Connection

Since the face detection technology is widely used, improving the detection speed and accuracy in face detection tasks has become a key challenge. Therefore, this paper takes the MTCNN model as the research object and makes improvements, the purpose of which is to optimize the detection speed and accuracy simultaneously. A dynamic min size algorithm is proposed. According to the size of the input image, it dynamically controls the minimum size of the face to be recognized by the model and reduces the number of iterations of the image pyramid, increasing the number of detection frames per second by 4 fps. Then, the standard convolution structure in the P-Net and R-Net models is replaced by the depth-wise separable convolution, which effectively reduces the number of parameters and computation of the model. Meanwhile, an O-Net model with a densely connected structure is also developed. Our experiments with well-known public datasets have demonstrated that the proposed network structure can improve the detection frame rate. By reusing the features of different levels of the image, the recall rate and the precision rate of the MTCNN model on the validation set are increased by 2.39% and 1.65%, respectively.