Attention-Aware Deep Reinforcement Learning for Video Face Recognition

Yongming Rao, Jiwen Lu, Jie Zhou; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3931-3940

Abstract


In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attention in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning framework without using extra labels. Unlike existing attention models, our method takes information from both the image space and the feature space as the input to make better use of face information that is discarded in the feature learning process. Besides, our approach is attention-aware, which seeks different attentions of videos for the verification of different pairs of videos. Our approach achieves very competitive video face recognition performance on three widely used video face datasets.

Related Material


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[bibtex]
@InProceedings{Rao_2017_ICCV,
author = {Rao, Yongming and Lu, Jiwen and Zhou, Jie},
title = {Attention-Aware Deep Reinforcement Learning for Video Face Recognition},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}