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.

使用深度强化学习方法，该论文提出了一种新颖的自动合成视频序列中年龄递进的面部图像的方法，它能够将给定对象的面部结构和纵向面部衰老过程相互连贯地建模，并使用深度卷积神经网络中的深层特征提取进行优化，是其它先前面部衰老方法无法比拟的解决方案。