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Facial Occlusion Detection via Structural Error Metrics and Clustering

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Intelligence Science and Big Data Engineering. Image and Video Data Engineering (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9242))

Abstract

Facial occlusions pose significant obstacles for robust face recognition in real-world applications. To eliminate the effect incurred by occlusions, most of the popular methods concentrate on dealing with the error between the occluded image and its recovery. Inspired by the working mechanism of human visual systems in facial occlusion detection, we suggest that it should be the error metric and clustering rather than exact recovery that play important roles for occlusion detection. By considering the structural differences between faces and occlusions, such as colors and textures, we construct five structural error metrics. By considering the common structures shared by all occlusions, such as localization and contiguity, we construct a structured clustering operator. Furthermore, we select the optimal error metric via the minimum occlusion boundary regularity criterion. Integrating the above techniques, we propose the Structural Error Metrics and Clustering (SEMC) algorithm for facial occlusion detection. Experimental results demonstrate that, even just using the mean face of the training images as the recovery image, SEMC still achieves more accurate and robust performance compared to the related state-of-the-art methods.

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Acknowledgment

This work is partially supported by National Science Foundation of China (61402411, 61379017), Zhejiang Provincial Natural Science Foundation (LY14F020015, LY14F020014), and Program for New Century Excellent Talents in University of China (NCET-12–1087).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Xiao-Xin Li & Jiaquan Gao

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Ronghua Liang & Haixia Wang

Authors
  1. Xiao-Xin Li
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  2. Ronghua Liang
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  3. Jiaquan Gao
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  4. Haixia Wang
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Corresponding author

Correspondence to Xiao-Xin Li .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Shaanxi, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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Li, XX., Liang, R., Gao, J., Wang, H. (2015). Facial Occlusion Detection via Structural Error Metrics and Clustering. In: He, X., et al. Intelligence Science and Big Data Engineering. Image and Video Data Engineering. IScIDE 2015. Lecture Notes in Computer Science(), vol 9242. Springer, Cham. https://doi.org/10.1007/978-3-319-23989-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-23989-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23987-3

  • Online ISBN: 978-3-319-23989-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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