  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.
