Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition


  • Jian Zhao National University of Singapore
  • Yu Cheng National University of Singapore
  • Yi Cheng Panasonic
  • Yang Yang National University of Singapore
  • Fang Zhao Inception Institute of Artificial Intelligence
  • Jianshu Li National University of Singapore
  • Hengzhu Liu National University of Defense Technology
  • Shuicheng Yan National University of Singapore
  • Jiashi Feng National University of Singapore




Despite the remarkable progress in face recognition related technologies, reliably recognizing faces across ages still remains a big challenge. The appearance of a human face changes substantially over time, resulting in significant intraclass variations. As opposed to current techniques for ageinvariant face recognition, which either directly extract ageinvariant features for recognition, or first synthesize a face that matches target age before feature extraction, we argue that it is more desirable to perform both tasks jointly so that they can leverage each other. To this end, we propose a deep Age-Invariant Model (AIM) for face recognition in the wild with three distinct novelties. First, AIM presents a novel unified deep architecture jointly performing cross-age face synthesis and recognition in a mutual boosting way. Second, AIM achieves continuous face rejuvenation/aging with remarkable photorealistic and identity-preserving properties, avoiding the requirement of paired data and the true age of testing samples. Third, we develop effective and novel training strategies for end-to-end learning the whole deep architecture, which generates powerful age-invariant face representations explicitly disentangled from the age variation. Extensive experiments on several cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the proposed AIM model over the state-of-the-arts. Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.




How to Cite

Zhao, J., Cheng, Y., Cheng, Y., Yang, Y., Zhao, F., Li, J., Liu, H., Yan, S., & Feng, J. (2019). Look across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9251-9258. https://doi.org/10.1609/aaai.v33i01.33019251



AAAI Technical Track: Vision