ZHANG Lihui. Mask occlusion face recognition algorithm based on neural network model with optimized loss function[J]. Journal of Mine Automation, 2024, 50(S1): 15-20.
Citation: ZHANG Lihui. Mask occlusion face recognition algorithm based on neural network model with optimized loss function[J]. Journal of Mine Automation, 2024, 50(S1): 15-20.

Mask occlusion face recognition algorithm based on neural network model with optimized loss function

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  • Received Date: January 24, 2024
  • [1]
    程德强, 寇旗旗, 江鹤, 等.全矿井智能视频分析关键技术综述[J].工矿自动化, 2023, 49(11):1-21.
    [2]
    王国法, 杜毅博, 任怀伟, 等.智能化煤矿顶层设计研究与实践[J].煤炭学报, 2020, 45(6):1909-1924.
    [3]
    DENG Jiankang, GUO Jia, YANG Jing, et al.ArcFace:additive angular margin loss for deep face recognition[Z/OL].[2023-12-10]. https://doi.org/10.48550/arXiv.1801.07698.
    [4]
    WANG Hao, WANG Yitong, ZHOU Zheng, et al.CosFace:large margin cosine loss for deep face recognition[C].IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018.DOI: 10.1109/CVPR.2018.00552.
    [5]
    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, Piscatawa, 2008:1-8.
    [6]
    JIA Hongjun, MARTINEZ A M.Support vector machines in face recognition with occlusions[C].IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009.DOI: 10.1109/CVPR.2009.5206862.
    [7]
    WU C Y, DING J J.Occluded face recognition using low-rank regression with generalized gradient direction[Z/OL].[2023-12-17]. http://arxiv.org/abs/1906.02429.
    [8]
    朱孟刚, 郑广海.遮挡人脸识别算法改进方法综述[J].计算机科学与应用, 2022, 12(6):1569-1579.
    [9]
    MONTERO D, NIETO M, AGINAKO N, et al.Boosting masked face recognition with multi-task arcface[C].The 16th International Conference on Signal-Image Technology & Internet-Based Systems, Dijon, 2022.DOI: 10.1109/SITIS57111.2022.00042.
    [10]
    FENG Tao, XU Liangpeng, YUAN Hangjie, et al.Towards mask-robust face recognition[C].IEEE/CVF International Conference on Computer Vision Workshops, Montreal, 2021.DOI: 10.1109/ICCVW54120.2021.00173.
    [11]
    BOUTROS F, DAMER N, KIRCHBUCHNER F, et al.Self-restrained triplet loss for accurate masked face recognition[Z/OL].[2023-12-17]. https://arxiv.org/abs/2103.01716v2.
    [12]
    DING Feifei, PENG Peixi, HUANG Yangru, et al.Masked face recognition with latent part detection[C].The 28th ACM International Conference on Multimedia, New York, 2020.DOI: 10.1145/3394171.3413731.
    [13]
    HE Lingxiao, LI Haiqing, ZHANG Qi, et al.Dynamic feature learning for partial face recognition[C].IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018.DOI: 10.1109/CVPR.2018.00737.
    [14]
    CAI Jiancheng, HAN Hu, CUI Jiyun.et al.Semi-supervised natural face de-occlusion[C].IEEE Transactions on Information Forensics and Security, 2020.DOI: 10.1109/TIFS.2020.3023793.
    [15]
    GENG Mengyue, PENG Peixi, HUANG Yangru, et al.Masked face recognition with generative data augmentation and domain constrained ranking[C].Medical Image Computing and Computer Assisted Intervention, 2020.
    [16]
    王国法.煤矿智能化最新技术进展与问题探讨[J].煤炭科学技术, 2022, 50(1):1-27.
    [17]
    刘晓阳, 霍祎炜.多尺度分区统一化LBP算子井下人员人脸识别方法[J].煤炭科学技术, 2019, 47(12):116-123.
    [18]
    陈岸明, 林群雄, 刘伟强.基于对比学习的多特征融合戴口罩人脸识别[J].计算机应用研究, 2024, 41(1):277-281, 287.
    [19]
    王均利, 李佳悦, 李秉天, 等.基于深度学习的煤矿井下低光照人脸检测方法[J].工矿自动化, 2023, 49(11):145-150.
    [20]
    DENG Jiankang, GUO Jia, VERVERAS E, et al.RetinaFace:single-shot multi-level face localization in the wild [C].IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, 2020.DOI: 10.1109/CVPR42600.2020.00525.
    [21]
    SCHROFF F, KALENICHENKO D, PHILBIN J.FaceNet:A uni-fied embedding for face recognition and clustering[C].IEEE Conference on Computer Vision and Pattern Recognition, Boston, 2015.DOI: 10.1109/CVPR.2015.7298682.
    [22]
    WANG Hao, WANG Yitong, ZHOU Zheng, et al.CosFace:large margin cosine loss for deep face recognition[C].IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018.DOI: 10.1109/CVPR.2018.00552.
    [23]
    DENG Jiankang, GUO Jia, ZAFEIRIOU S.ArcFace:additive angular margin loss for deep face recognition[Z/OL].[2023-12-20]. https://arxiv.org/abs/1801.07698.
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