Mask-wearing Face Recognition Method Fused with Dual Attention Mechanism

Abstract

Abstract: To address the problem that existing face recognition models cannot effectively extract regional features from faces wearing masks， a face recognition model incorporating a dual attention mechanism is proposed for faces wearing masks. Firstly， a self-constructed face image wearing a mask is used as input data， and ResNet50 is used as the benchmark network to introduce coordinate attention and split attention mechanisms into the residual blocks， where coordinate attention is used to reduce feature extraction in the mask region and reduce feature interference in the mask region； Split attention is used to extract non-mask region features at a fine granularity and extract more features from key areas. The ArcFace classification function is then used to optimize the classification boundary， combined with a cross-entropy loss function as a constraint， to achieve fine-grained recognition of faces wearing masks. The experimental results show that the model in this paper achieves 95.2% recognition accuracy in the test set， which is 1 percent point and 1.5 percent point higher than that of ResNet50 and AttentionNet models respectively.

Key words: mask-wearing face recognition model, coordinate attention, split attention, ArcFace classification function, cross-entropy loss function

SHENG Jiang-an, CHEN Shu-rong. Mask-wearing Face Recognition Method Fused with Dual Attention Mechanism[J]. Computer and Modernization, 2023, 0(02): 72-77.

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