Unsupervised facial image occlusion detection with deep autoencoder

Xu-dong Wang; Hong-quan Wei; Shao-mei Li; Chao Gao; Rui-yang Huang

doi:10.1117/12.2540135

14 August 2019 Unsupervised facial image occlusion detection with deep autoencoder

Xu-dong Wang, Hong-quan Wei, Shao-mei Li, Chao Gao, Rui-yang Huang

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111792F (2019) https://doi.org/10.1117/12.2540135
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Xu-dong Wang, Hong-quan Wei, Shao-mei Li, Chao Gao, and Rui-yang Huang "Unsupervised facial image occlusion detection with deep autoencoder", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111792F (14 August 2019); https://doi.org/10.1117/12.2540135

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