{"type":"ScholarlyArticle","title":"Dual Conditional GANs for Face Aging and Rejuvenation","authors":[{"name":"Jingkuan Song","slug":"jingkuan-song","given":"Jingkuan","family":"Song","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Jingqiu Zhang","slug":"jingqiu-zhang","given":"Jingqiu","family":"Zhang","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Lianli Gao","slug":"lianli-gao","given":"Lianli","family":"Gao","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"Beihang University , Beijing , 100083 , China,"},{"name":"Heng Tao Shen","slug":"heng-tao-shen","given":"Heng Tao","family":"Shen","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , Chengdu 611731 , China"}],"abstract":"Face aging and rejuvenation is to predict the face of a person at different ages. While tremendous progress have been made in this topic, there are two central problems remaining largely unsolved: 1)\r\nthe majority of prior works requires sequential training data, which is very rare in real scenarios, and 2) how to simultaneously render aging face and preserve personality.\r\nTo tackle these issues, in this paper, we develop a novel dual conditional GAN (DCGAN) mechanism, which enables face aging and rejuvenation to be trained from multiple sets of unlabeled face images with different ages.\r\nIn our architecture, the primal conditional GAN transforms a face image to other ages based on the age condition, while the dual conditional GAN learns to invert the task.\r\nHence a loss function that accounts for the reconstruction error of images can preserve the personal identity, while the discriminators on the generated images learn the transition patterns (e.g., the shape and texture changes between age groups) and guide the generation of age-specific photo-realistic faces.\r\nExperimental results on two publicly dataset demonstrate the appealing performance of the proposed framework by comparing with the state-of-the-art methods.","doi":"10.24963/ijcai.2018/125","url":"https://ijcai.org/proceedings/2018/125","pdf":"https://ijcai.org/proceedings/2018/0125.pdf","markdown":"https://ijcai.org/proceedings/2018/125.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/125","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Computer Vision","track":"Main track","number":125,"pages":{"first":899,"last":905},"keywords":["Machine Learning: Machine Learning","Computer Vision: Computer Vision","Machine Learning Applications: Applications of Unsupervised Learning","Computer Vision: Biometrics, Face and Gesture Recognition"],"videos":[],"references":[{"position":1,"raw":"[Antipov et al., 2017] Grigory Antipov, Moez Baccouche, and Jean-Luc Dugelay. 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