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Proceedings Paper

Unsupervised facial image occlusion detection with deep autoencoder
Author(s): Xu-dong Wang; Hong-quan Wei; Shao-mei Li; Chao Gao; Rui-yang Huang
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Paper Abstract

Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still a challenging problem. Although there are many works to solve the problem of obscuring the face, the occlusion is still a challenge in face recognition. To overcome this issue, firstly we should detect the occlusion position in the facial images. We construct a robust self-encoding machine to solve the occlusion detection problem in face images and uses synthetic occlusion data for training. We evaluated our method under various synthetic occlusion face images. Experiments show that our method can effectively detect various types of occlusion masks in an unsupervised manner and has better robustness to the occlusion categories.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792F (14 August 2019); doi: 10.1117/12.2540135
Show Author Affiliations
Xu-dong Wang, National Digital Switching System Engineering and Technological Research Ctr. (China)
Hong-quan Wei, National Digital Switching System Engineering and Technological Research Ctr. (China)
Shao-mei Li, National Digital Switching System Engineering and Technological Research Ctr. (China)
Chao Gao, National Digital Switching System Engineering and Technological Research Ctr. (China)
Rui-yang Huang, National Digital Switching System Engineering and Technological Research Ctr. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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