关键词: 卷积神经网络, 人脸检测, 多尺度, 全卷积网络, 特征提取

Abstract: The convolutional neural network model needs amount of computation to detect faces at different scales. The process consists of several parts, which is too complex. In view of these two deficiencies, a multi-scale convolution neural network is proposed. According to each layer of convolutional neural network with different sizes of receptive field, it extracts the feature vector of a plurality of layers respectively for face classification and regression, and changes the full-connected layers into convolutional layers in order to adapt to different sizes of picture. In this method, multiple steps of face detection are integrated into a convolutional neural network, and the complexity of the model is reduced. Experimental results show that the above improvements can effectively improve the detection performance of the model. Under the same testing conditions, the proposed model has a significant improvement in terms of accuracy and detection speed compared to other face detection models.

Key words: convolutional neural network, face detection, multi-scale, full convolutional network, feature extraction