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arXiv · 1811.11082

Automatic Face Aging in Videos via Deep Reinforcement Learning

Chi Nhan Duong·Khoa Luu·Kha Gia Quach·Nghia Nguyen·Eric Patterson·Tien D. Bui·Ngan Le
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Abstract

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.

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Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Nghia Nguyen, Eric Patterson, Tien D. Bui, Ngan Le. 2018-11-27. Automatic Face Aging in Videos via Deep Reinforcement Learning. https://arxiv.org/abs/1811.11082

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