Lejian Ren
Shuicheng Yan
ABSTRACT
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, finding lost children, and entertainments. In recent years, face aging has witnessed various breakthroughs and a number of face aging models have been proposed. Face aging, however, is still a very challenging task in practice for various reasons. First, faces may have many different 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.
Supplemental Material
- 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 ICCV. Google Scholar
Digital Library
- Si Liu, Yao Sun, Wei Wang, Defa Zhu, Xiangbo Zhu, and Shuicheng Yan. 2017. Face Aging with Contextual Generative Adversarial Nets ACM MM. Google ScholarDigital Library
- Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks NIPS. Google ScholarDigital Library
Index Terms
- Time Traveler: A Real-time Face Aging System
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