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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

From Eyes to Face synthesis: a new approach for human-centered smart surveillance

Chen, Xiang, Qing, Linbo, He, Xiaohai, Su, Jie and Peng, Yonghong (2018) From Eyes to Face synthesis: a new approach for human-centered smart surveillance. IEEE Access, 6 (99). 14567 -14575. ISSN 2169-3536

Item Type: Article


With the popularity of surveillance cameras and the development of deep learning, significant progress has been made in the field of smart surveillance. Face recognition is one of the most important yet challenging tasks in human-centered smart surveillance, especially in public security, criminal investigation and anti-terrorism, and so on. Although, the state-of-the-art algorithms for face recognition have achieved dramatically improved results and have been widely applied in authentication scenario, the occlusion problem on face is still one of the critical issues for personal identification in smart surveillance, especially in the occasion of terrorist searching and identification. To address this issue, this paper proposed a new approach for eyes-to-face synthesis and personal identification for human-centered smart surveillance. An end-toend network based on conditional generative adversarial networks (GAN) is designed to generate the face information based only on the available data of eyes region. To obtain photorealistic faces and identitypreserving information, a synthesis loss function based on feature loss, GAN loss, and total variation loss is proposed to guide the training process. Both the subject and objective experimental results demonstrated that the proposed method can preserve the identity based on eyes-only data, and provide a potential solution for the identification of person even in the case of face occlusion.

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Depositing User: Barry Hall


Item ID: 8956
Identification Number:
ISSN: 2169-3536
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Date Deposited: 15 Mar 2018 09:02
Last Modified: 30 Sep 2020 11:04


Author: Xiang Chen
Author: Linbo Qing
Author: Xiaohai He
Author: Jie Su
Author: Yonghong Peng

University Divisions

Faculty of Technology
Faculty of Technology > FOT Executive


Computing > Data Science

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