ARFace: Attention-Aware and Regularization for Face Recognition With Reinforcement Learning

Liping Zhang, Linjun Sun, Lina Yu, Xiaoli Dong, Jinchao Chen, Weiwei Cai, Chen Wang, Xin Ning

科研成果: 期刊稿件文章同行评审

51 引用 (Scopus)

摘要

Different face regions have different contributions to recognition. Especially in the wild environment, the difference of contributions will be further amplified due to a lot of interference. Based on this, this paper proposes an attention-aware face recognition method based on a deep convolutional neural network and reinforcement learning. The proposed method composes of an Attention-Net and a Feature-net. The Attention-Net is used to select patches in the input face image according to the facial landmarks and trained with reinforcement learning to maximize the recognition accuracy. The Feature-net is used for extracting discriminative embedding features. In addition, a regularization method has also been introduced. The mask of the input layer is also applied to the intermediate feature maps, which is an approximation to train a series of models for different face patches and provide a combined model. Our method achieves satisfactory recognition performance on its application to the public prevailing face verification database.

源语言英语
页(从-至)30-42
页数13
期刊IEEE Transactions on Biometrics, Behavior, and Identity Science
4
1
DOI
出版状态已出版 - 1 1月 2022

指纹

探究 'ARFace: Attention-Aware and Regularization for Face Recognition With Reinforcement Learning' 的科研主题。它们共同构成独一无二的指纹。

引用此