Time Traveler: a real-time face aging system


  1. Lejian Ren, Si Liu, Yao Sun, Jian Dong, Luoqi Liu, Shuicheng Yan


    Face aging, also known as age progression, is attracting more and more research interests. It has plenty of applications in various domains including cross-age face recognition, nding lost children, and entertainments. In recent years, face aging has witnessed various breakthroughs and a number of face aging models have been proposed [3]. Face aging, however, is still a very challenging task in practice for various reasons. First, faces may have many dierent expressions and lighting conditions, which pose great challenges to modeling the aging patterns. Besides, the training data are usually very limited and the face images for the same person only cover a narrow range of ages.




PPT(pwd: ic9u)


  • [1] Dlib C++ Library. http://dlib.net/.
  • [2] Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox. 2015. Flownet: Learning optical flow with convolutional networks. In ICCV.
  • [3] Y. Fu, G. Guo, and T. S. Huang. 2010. Age synthesis and estimation via faces: a survey. TPAMI (2010)
  • [4] Si Liu, Xinyu Ou, Ruihe Qian, Wei Wang, and Xiaochun Cao. 2016. Makeup like a superstar: Deep localized makeup transfer network. IJCAI (2016).
  • [5] Si Liu, Yao Sun, Wei Wang, Defa Zhu, Xiangbo Zhu, and Shuicheng Yan. 2017. Face Aging with Contextual Generative Adversarial Nets. In ACM MM.
  • [6] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS.
  • [7] Zhen Wei, Yao Sun, Jinqiao Wang, Hanjiang Lai, and Si Liu. 2017. Learning Adaptive Receptive Fields for Deep Image Parsing Network. In CVPR.
  • [8] Tianzhu Zhang, Changsheng Xu, and Ming-Hsuan Yang. 2017. Multi-task Correlation Particle Filter for Robust Visual Tracking. In CVPR.