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

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)30-42
Number of pages13
JournalIEEE Transactions on Biometrics, Behavior, and Identity Science
Volume4
Issue number1
DOIs
StatePublished - 1 Jan 2022

Keywords

  • Attention-aware
  • face recognition
  • regularization
  • reinforcement learning

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