Largest Matching Areas for Illumination and Occlusion Robust Face Recognition
提出一种基于最大匹配区域的人脸识别方法，同时解决光照不均、局部遮挡和训练数据有限三个挑战，仅用单张训练图像即可超越或匹敌需多张图像的方法。
In this paper, we introduce a novel approach to face recognition which simultaneously tackles three combined challenges: (1) uneven illumination; (2) partial occlusion; and (3) limited training data. The new approach performs lighting normalization, occlusion de-emphasis and finally face recognition, based on finding the largest matching area (LMA) at each point on the face, as opposed to traditional fixed-size local areabased approaches. Robustness is achieved with novel approaches for feature extraction, LMA-based face image comparison and unseen data modeling. On the extended YaleB and AR face databases for face identification, our method using only a single training image per person, outperforms other methods using a single training image, and matches or exceeds methods which require multiple training images. On the labeled faces in the wild face verification database, our method outperforms comparable unsupervised methods. We also show that the new method performs competitively even when the training images are corrupted.