Although face recognition methods in controlled environments have achieved high accuracy results, there#R##N##R##N#are still problems in real-life situations. Some of the challenges include changes in face expressions, pose,#R##N##R##N#lighting conditions or presence of occlusion. There were several efforts for tackling the occlusion problem,#R##N##R##N#mainly by learning discriminating features from non-occluded faces for occluded faces recognition. In this#R##N##R##N#paper, we propose the reversed process, to learn from the occluded faces for the purpose of non-occluded#R##N##R##N#faces recognition. This process has several useful applications, such as in suspects identification and person#R##N##R##N#re-identification. Correlation filters are constructed from training images (occluded faces) images of each#R##N##R##N#person, which are used later for the classification of input images (non-occluded faces). In addition, the use of#R##N##R##N#skin masks with the correlation filters is investigated.
['Fares Saleh Al-qunaieer', 'Mohamed I. Alkanhal']
Learning from Partially Occluded Faces