Automatic Face Aging in Videos via Deep Reinforcement Learning
This addresses the need for realistic and coherent face aging in videos, which is incremental over single-image methods.
The paper tackles the problem of automatically synthesizing age-progressed facial images in video sequences, achieving consistent facial features across frames and preserving visual identity, as demonstrated on the AGFW-v2 database with improvements in quality, temporal smoothness, and cross-age face verification.
This paper presents a novel approach to synthesize automatically age-progressed facial images in video sequences using Deep Reinforcement Learning. The proposed method models facial structures and the longitudinal face-aging process of given subjects coherently across video frames. The approach is optimized using a long-term reward, Reinforcement Learning function with deep feature extraction from Deep Convolutional Neural Network. Unlike previous age-progression methods that are only able to synthesize an aged likeness of a face from a single input image, the proposed approach is capable of age-progressing facial likenesses in videos with consistently synthesized facial features across frames. In addition, the deep reinforcement learning method guarantees preservation of the visual identity of input faces after age-progression. Results on videos of our new collected aging face AGFW-v2 database demonstrate the advantages of the proposed solution in terms of both quality of age-progressed faces, temporal smoothness, and cross-age face verification.