A Mask-Wearing Face Recognition Method Based on Low-Level Features and Deep Residual Networks

Masks will invalidate the original face recognition algorithm model and make the computer unable to recognize faces. In addition, there are many types of masks, and the degree of occlusion is different, which increases the difficulty of face recognition. This paper combines the traditional feature extraction method with deep learning, and proposes a face recognition method with masks based on low-level features and deep residual network. The method of face segmentation based on feature points is used to extract the local features of the face, using the Holistically-nested Edge Detection (HED) algorithm to extract the overall contour features of the face, fusion of local features, overall contour features and pre-processed images into a deep residual network model, realize face recognition with masks, and evaluate the face recognition method with accuracy. The experiment results show this method improves the recognition accuracy compared with Principal Component Analysis (PCA) and convolutional neural network (CNN).