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; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 10013-10022

Abstract


This paper presents a novel approach for synthesizing 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.

Related Material


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[bibtex]
@InProceedings{Duong_2019_CVPR,
author = {Duong, Chi Nhan and Luu, Khoa and Quach, Kha Gia and Nguyen, Nghia and Patterson, Eric and Bui, Tien D. and Le, Ngan},
title = {Automatic Face Aging in Videos via Deep Reinforcement Learning},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}