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Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China
Face occlusion such as masked face is a challenge problem for most face detection algorithms due to a lack of discriminative information. In this paper, we proposed a novel method to address occluded face detection especially masked face detection. We built a masked face database whose images are collected from web images in the wild. The database includes more than 6000 images and more than 10000 masked faces. To perform masked face detection, we proposed a joint pre-detection and classification method, which learn a discriminative classifier based on deep learning to classify the face proposals which are generated by some weak face detectors. The classifier has higher discrimination power to masked face, unmasked face and non-face. Experimental comparisons with state-of-the-art face detection methods show that the proposed method can give better performance. .
Keywords:Image Processing;Occlusion Face Detection;Deep Learning;Convolutional Neural Network