16 May 2017 Varying face occlusion detection and iterative recovery for face recognition
Author Affiliations +
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.
© 2017 SPIE and IS&T
Meng Wang, Meng Wang, Zhengping Hu, Zhengping Hu, Zhe Sun, Zhe Sun, Shuhuan Zhao, Shuhuan Zhao, Mei Sun, 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 . Submission: Received: 21 December 2016; Accepted: 26 April 2017
Received: 21 December 2016; Accepted: 26 April 2017; Published: 16 May 2017
JOURNAL ARTICLE
8 PAGES


SHARE
Back to Top