Research on Face Detection Technology Based on MTCNN
Ning Zhang, Junmin Luo, Wuqi Gao
Abstract
Face detection is an important research direction in the field of target detection. For the input image, the position of the face is returned. In order to complete the task of face detection using deep learning, data input, feature extraction and face feature detection are three steps, among which feature extraction is the most important part. By studying the basic principles of current mainstream target detection algorithms, this paper compares the characteristics of Two-stage and One-stage detection models and their application in face detection tasks. At the same time, MTCNN(Multi-task convolution neural network) is deeply analyzed and its implementation principle is introduced in detail. The real effect of MTCNN in face detection task is verified by experiments. The results of the model are compared with those of yolov3 model in the wider face dataset.