标题:A hierarchically trained generative network for robust facial symmetrization
作者:Zhang, Shu; Wang, Ting; Peng, Yanjun; Dong, Junyu
通讯作者:Wang, T
作者机构:[Wang, Ting; Peng, Yanjun] Shandong Univ Sci & Technol, Qingdao, Shandong, Peoples R China.; [Zhang, Shu; Dong, Junyu] Ocean Univ China, Qingdao, Sh 更多
会议名称:7th International Conference on Biomedical Engineering and Biotechnology (iCBEB)
会议日期:OCT 17-20, 2018
来源:TECHNOLOGY AND HEALTH CARE
出版年:2019
卷:27
页码:S217-S227
DOI:10.3233/THC-199021
关键词:Facial symmetry; facial palsy image; symmetrization; multi-stage; training; Generative Adversarial Networks
摘要:Face symmetrization has extensive applications in both medical and academic fields, such as facial disorder diagnosis. Human face possesses an important characteristic, which is as known as symmetry. However, in many scenarios, the perfect symmetry doesn't exist in human faces, which yields a large number of studies around this topic. For example, facial palsy evaluation, facial beauty evaluation based on facial symmetry analysis, and many among others. Currently, there are still very limited researches dedicated for automatic facial symmetrization. Most of the existing studies only utilized their own implantations for facial symmetrization to assist their interdisciplinary academic researches. Limitations thus can be noticed in their methods, such as the requirements for manual interventions. Furthermore, most existing methods utilize facial landmark detection algorithms for automatic facial symmetrization. Though accuracies of the landmark detection algorithms are promising, the uncontrolled conditions in the facial images can still negatively impact the performance of the symmetrical face production. To this end, this paper presents a joint-loss enhanced deep generative network model for automatic facial symmetrization, which is achieved by a full facial image analysis. The joint-loss consists of a pair of adversarial losses and an identity loss. The adversarial losses try to make the generated symmetrical face as realistic as possible, while the identity loss helps to constrain the output to have the same identity of the person in the original input as much as possible. Rather than an end-to-end learning strategy, the proposed model is constructed by a multi-stage training process, which avoids the demand for a large size of the symmetrical face as training data. Experiments are conducted with comparisons with several existing methods based on some of the most popular facial landmark detection algorithms. Competitive results of the proposed method are demonstrated.
收录类别:CPCI-S;SCOPUS;SCIE
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068309025&doi=10.3233%2fTHC-199021&partnerID=40&md5=4364576861b18867600831e02ff660ea
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