现代信息科技2026，Vol.10Issue(17)：26-30,5.DOI:10.19850/j.cnki.2096-4706.2026.17.005
基于深度学习的遮挡人脸识别方法研究
Research on Occluded Face Recognition Methods Based on Deep Learning
衣姿好 1张烜赐 1李涵若水 1王雨霏 1张豪1
作者信息
- 1. 中国矿业大学(北京),北京 100083
- 折叠
摘要
Abstract
To address the issue that wearing masks results in the substantial loss of key facial features,significantly degrading the performance of traditional face recognition systems,this study proposes an occluded face recognition method that combines the Inception-ResNet architecture with the CBAM attention mechanism.First,the Inception module is employed to extract features across multiple receptive fields,while the residual mechanism of ResNet ensures the stability of the deep network.Next,the CBAM attention mechanism refines the features along both channel and spatial dimensions.Finally,the ArcFace loss function is used to optimize the angular relationship between feature vectors and classification weights,enabling the model to learn more discriminative facial features.Experimental results demonstrate that the proposed method achieves superior recognition accuracy and occlusion robustness in masked face scenarios.关键词
遮挡人脸识别/深度学习/Inception-ResNet/注意力机制/ArcFaceKey words
occluded face recognition/Deep Learning/inception-ResNet/attention/ArcFace分类
信息技术与安全科学引用本文复制引用
衣姿好,张烜赐,李涵若水,王雨霏,张豪..基于深度学习的遮挡人脸识别方法研究[J].现代信息科技,2026,10(17):26-30,5.
