Varying face occlusion detection and iterative recovery for face recognition

Meng Wang; Zhengping Hu; Zhe Sun; Shuhuan Zhao; Mei Sun

doi:10.1117/1.JEI.26.3.033009

16 May 2017 Varying face occlusion detection and iterative recovery for face recognition

Meng Wang, Zhengping Hu, Zhe Sun, Shuhuan Zhao, Mei Sun

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 3, 033009 (May 2017). https://doi.org/10.1117/1.JEI.26.3.033009

Abstract

In most sparse representation methods for face recognition (FR), occlusion problems were usually solved via removing the occlusion part of both query samples and training samples to perform the recognition process. This practice ignores the global feature of facial image and may lead to unsatisfactory results due to the limitation of local features. Considering the aforementioned drawback, we propose a method called varying occlusion detection and iterative recovery for FR. The main contributions of our method are as follows: (1) to detect an accurate occlusion area of facial images, an image processing and intersection-based clustering combination method is used for occlusion FR; (2) according to an accurate occlusion map, the new integrated facial images are recovered iteratively and put into a recognition process; and (3) the effectiveness on recognition accuracy of our method is verified by comparing it with three typical occlusion map detection methods. Experiments show that the proposed method has a highly accurate detection and recovery performance and that it outperforms several similar state-of-the-art methods against partial contiguous occlusion.

Citation Download Citation

Meng Wang, Zhengping Hu, Zhe Sun, Shuhuan Zhao, and Mei Sun "Varying face occlusion detection and iterative recovery for face recognition," Journal of Electronic Imaging 26(3), 033009 (16 May 2017). https://doi.org/10.1117/1.JEI.26.3.033009

Received: 21 December 2016; Accepted: 26 April 2017; Published: 16 May 2017

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