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.

References ［27］

［1］	李小薪，梁荣华. 有遮挡人脸识别综述:从子空间回归到深度学习［J］. 计算机学报， 2018，41(1):177-207.
［2］	徐润昊，程吉祥，李志丹，等. 基于循环生成对抗网络的含遮挡人脸识别［J］. 计算机工程， 2022，48(5):289-296.
［3］	LI Y D， GUO K， LU Y G， et al. Cropping and attention based approach for masked face recognition［J］. Applied Intelligence， 2021，51(5):3012-3025.
［4］	WANG F， JIANG M Q， QIAN C， et al. Residual attention network for image classification［C］// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:6450-6458.
［5］	HU J， SHEN L， SUN G. Squeeze-and-excitation networks［C］// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:7132-7141.
［6］	PARK J， WOO S， LEE J， et al. BAM: Bottleneck attention module［J］. arXiv preprint arXiv:1807.06514， 2018.
［7］	WOO S， PARK J， LEE J， et al. CBAM: Convolutional block attention module［C］// Computer Vision-ECCV 2018. 2018:3-19.
［8］	SHAO Z W， LIU Z L， CAI J F， et al. Deep adaptive attention for joint facial action unit detection and face alignment［C］// Computer Vision – ECCV 2018. 2018:725-740.
［9］	RAO Y M， LU J W， ZHOU J. Attention-Aware deep reinforcement learning for video face recognition［C］// 2017 IEEE International Conference on Computer Vision (ICCV). 2017:3951-3960.
［10］	HARIRI W. Efficient masked face recognition method during the COVID-19 Pandemic［J］. arXiv preprint arXiv:2105.03026， 2021.
［11］	WENG R L， LU J W， TAN Y P. Robust point set matching for partial face recognition［J］. IEEE Transactions on Image Processing， 2016，25(3):1163-1176.
［12］	HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:770-778.
［13］	HOU Q B， ZHOU D Q， FENG J S. Coordinate attention for efficient mobile network design［C］// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2021:13708-13717.
［14］	ZHANG H， ZU K K， LU J， et al. EPSANet: An efficient pyramid split attention block on convolutional neural network［J］. arXiv preprint arXiv:2105.14447， 2021.
［15］	DENG J K， GUO J， XUE N N， et al. ArcFace: Additive angular margin loss for deep face recognition［C］// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:4685-4694.
［16］	SANTURKAR S， TSIPRAS D， ILYAS A， et al. How does batch normalization help optimization?［C］// NIPS'18: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018:2488-2498.
［17］	HE K M， ZHANG X Y， REN S Q， et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification［C］// 2015 IEEE International Conference on Computer Vision (ICCV). 2015:1026-1034.
［18］	AGARAP A F. Deep learning using rectified linear units (ReLU)［J］. arXiv preprint arXiv:1803.08375， 2018.
［19］	JANG E， GU S X， POOLE B. Categorical Reparameterization with gumbel-softmax［J］. arXiv preprint arXiv:1611.01144， 2016.
［20］	YI D， LEI Z， LIAO S C， et al. Learning face representation from scratch［J］. arXiv preprint arXiv:1411.7923， 2014.
［21］	CAO Q， SHEN L， XIE W D， et al. VGGFace2: A dataset for recognising faces across pose and age［C］// 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). 2018:67-74.
［22］	HUANG G B， MATTAR M， BERG T， et al. Labeled faces in the wild: A database for studying face recognition in unconstrained environments［C］//Workshop on faces in Real-Life Images: Detection， Alignment， and Recognition. 2008.
［23］	ANWAR A， RAYCHOWDHURY A. Masked face recognition for secure authentication［J］. arXiv preprint arXiv:2008.11104， 2020.
［24］	KING D E. Dlib-ml: A machine learning toolkit［J］. Journal of Machine Learning Research， 2009，10:1755-1758.
［25］	ZHANG Q， YANG Y. SA-Net: Shuffle Attention for Deep Convolutional Neural Networks［C］// ICASSP 2021 - 2021 IEEE International Conference on Acoustics， Speech and Signal Processing (ICASSP). 2021:2235-2239.
［26］	CHEN S， LIU Y， GAO X， et al. MobileFaceNets: Efficient CNNs for accurate real-time face verification on mobile devices［C］// Biometric Recognition. 2018:428-438.
［27］	SELVARAJU R R， COGSWELL M， DAS A， et al. Grad-CAM: Visual explanations from deep networks via gradient-based localization［C］// 2017 IEEE International Conference on Computer Vision (ICCV). 2017:618-626.