NDLI: A Human Visual Experience-Inspired Similarity Metric for Face Recognition Under Occlusion

Content Provider	Springer Nature Link
Author	Mi, Jian Xun Li, Chao Li, Cong Liu, Tao Liu, Ying
Copyright Year	2016
Abstract	Recognizing an occluded face is a challenging task for face recognition systems. Although many methods for dealing with occlusion have been proposed, it is more attractive to build a robust face recognition system that focuses on non-occluded regions. Such systems automatically ignore occluded parts, which is broadly consistent with the human visual experience. Based on this idea, a new similarity metric called the average degree of aggregation of matched pixels (ADAMP) is proposed. The discrimination performance of ADAMP is derived from information about the spatial distribution of matched pixels.The proposed method is evaluated with extensive experiments. Compared with state-of-the-art methods, our method is very competitive in terms of recognition accuracy and computation time. In particular, recognition rates of 99.5 % in the presence of sunglasses and 96.5 % in the presence of scarves can be achieved on a benchmark dataset.Although ADAMP is relatively simple and has the same time complexity as the Euclidean distance, it is demonstrated to be very robust against occlusion. Recognition results using ADAMP are very competitive with those given by state-of-the-art methods.
Starting Page	818
Ending Page	827
Page Count	10
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	8
Issue Number	5
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2016-07-18
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Face recognition Occlusion Face image matching Similarity metric Robust classification Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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