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Varying face occlusion detection and iterative recovery for face recognition

[+] Author Affiliations
Meng Wang

Yanshan University, School of Information Science and Engineering, Qinhuangdao, China

Taishan University, School of Physics and Electronic Engineering, Tai’an, China

Zhengping Hu, Zhe Sun

Yanshan University, School of Information Science and Engineering, Qinhuangdao, China

Shuhuan Zhao

Hebei University, College of Electronic Information Engineering, Baoding, China

Mei Sun

Taishan University, School of Physics and Electronic Engineering, Tai’an, China

J. Electron. Imaging. 26(3), 033009 (May 16, 2017). doi:10.1117/1.JEI.26.3.033009
History: Received December 21, 2016; Accepted April 26, 2017
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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.

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Citation

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


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