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ARFace: Attention-Aware and Regularization for Face Recognition With Reinforcement Learning | IEEE Journals & Magazine | IEEE Xplore

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


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...Show More

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.
Page(s): 30 - 42
Date of Publication: 11 August 2021
Electronic ISSN: 2637-6407

Funding Agency:


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