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

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Abstract

Background/Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

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Funding

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).

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi, Chao Li, Cong Li, Tao Liu & Ying Liu

  2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Jian-Xun Mi, Chao Li, Cong Li, Tao Liu & Ying Liu

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Jian-Xun Mi

Authors
  1. Jian-Xun Mi
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  2. Chao Li
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  3. Cong Li
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  4. Tao Liu
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  5. Ying Liu
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Correspondence to Jian-Xun Mi.

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Conflict of interest

Jian-Xun Mi, Chao Li, Cong Li, Tao Liu and Ying Liu declare that they have no conflict of interest.

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Informed consent was not required as no human or animals were involved.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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Mi, JX., Li, C., Li, C. et al. A Human Visual Experience-Inspired Similarity Metric for Face Recognition Under Occlusion. Cogn Comput 8, 818–827 (2016). https://doi.org/10.1007/s12559-016-9420-x

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  • DOI: https://doi.org/10.1007/s12559-016-9420-x

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