A Human Visual Experience-Inspired Similarity Metric for Face Recognition Under Occlusion
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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.
KeywordsFace recognition Occlusion Face image matching Similarity metric Robust classification
This study was funded by the National Nature Science Foundation of China (Grant Nos. 61202276 and 61403053), Chongqing Natural Science Foundation (Project Nos. cstc2014jcyjA40018 and cstc2014kjrcqnrc40002), and Chongqing Education Committee (Grant Nos. KJ1500402 and KJ1500417).
Compliance with Ethical Standards
Conflict of interest
Jian-Xun Mi, Chao Li, Cong Li, Tao Liu and Ying Liu declare that they have no conflict of interest.
Informed consent was not required as no human or animals were involved.
Human and Animal Rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
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