文献类型：期刊文献

英文题名：Facial Landmark Detection Using Generative Adversarial Network Combined with Autoencoder for Occlusion

作者：Liu, Hongzhe[1];Zheng, Weicheng[1];Xu, Cheng[1];Liu, Teng[1];Zuo, Min[2]

第一作者：刘宏哲

通讯作者：Xu, C[1]

机构：[1]Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China;[2]Beijing Technol & Business Univ, Natl Engn Lab Agriprod Qual Traceabil, Beijing, Peoples R China

第一机构：北京联合大学机器人学院|北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing Key Lab Informat Serv Engn, Beijing, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;[11417103]北京联合大学北京市信息服务工程重点实验室;

年份：2020

卷号：2020

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：;EI(收录号：20205109623734);Scopus(收录号：2-s2.0-85097573254);WOS:【SCI-EXPANDED(收录号:WOS:000598346300004)】；

基金：This work was supported by the National Natural Science Foundation of China (grant nos. 61871039, 61932012, 61802019, and 61906017), the Beijing Municipal Commission of Education Project (Nos. KM202111417001 and KM201911417001), National Engineering Laboratory for Agri-Product Quality Traceability Project (No. AQT-2020-YB2), the Supporting Plan for Cultivating High Level Teachers in Colleges and Universities in Beijing (grant no. IDHT20170511), and the Academic Research Projects of Beijing Union University (grant nos. ZK80202001, XP202015, BPHR2020EZ01, BPHR2019AZ01, 202011417004, 202011417005, 202011417SJ025, and KYDE40201702).

语种：英文

外文关键词：Learning systems

摘要：The performance of the facial landmark detection model will be in trouble when it is under occlusion condition. In this paper, we present an effective framework with the objective of addressing the occlusion problem for facial landmark detection, which includes a generative adversarial network with improved autoencoders (GAN-IAs) and deep regression networks. In this model, GAN-IA can restore the occluded face region by utilizing skip concatenation among feature maps to keep more details. Meanwhile, self-attention mechanism that is effective in modeling long-range dependencies is employed to recover harmonious images for occluded faces. Deep regression networks are used to learn a nonlinear mapping from facial appearance to facial shape. Benefited from the mutual cooperation of GAN-IA and deep regression networks, a robust facial landmark detection model is achieved for the occlusion problem and the performance of the model achieves obviously improvement on challenging datasets.