参考文献/References:
[1] DENG Jiankang,GUO Jia,ZHANG Debing,et al.Lightweight face recognition challenge[EB/OL].[2021-11-17].https://ieeexplore.ieee.org/document/9022288.
[2] MÉNDEZ-VÁZQUEZ H,MARTÍNEZ-DÍAZ Y,NICOLÁS-DÍAZ M,et al.Bench-marking lightweight face architectures on specific face recognition scenarios[J].Artificial Intelligence Review,2021,54(8):6201-6244.
[3] SANDLER M,HOWARD A,ZHU Menglong,et al.MobileNet V2:inverted residuals and linear bottlenecks[EB/OL].[2021-11-17].https://arxiv.org/pdf/1801.04381v3.pdf.
[4] MA Ningning,ZHANG Xiangyu,ZHENG Haitao,et al.ShuffleNet V2:practical guidelines for efficient CNN architecture design[EB/OL].[2021-11-17].https://arxiv.org/pdf/1807.11164.pdf.
[5] ZHANG Qian,LI Jianjun,YAO Meng,et al.VarGNet:variable group convolutional neural network for efficient embedded computing[EB/OL].[2021-11-17].https://arxiv.org/abs/1907.05653.
[6] CHEN Sheng,LIU Yang,GAO Xiang,et al.MobileFaceNets:efficient CNNs for accurate real-time face verification on mobile devices[EB/OL].[2021-11-17].https://arxiv.org/ftp/arxiv/papers/1804/1804.07573.pdf.
[7] HOWARD A G,ZHU Menglong,CHEN Bo,et al.MobileNets:efficient convolutional neural networks for mobile vision applications[EB/OL].[2021-11-17].https://arxiv.org/pdf/1704.04861.pdf.
[8] LI Xianyang,WANG Feng,HU Qinghao,et al.AirFace:lightweight and efficient model for face recognition[EB/OL].[2021-11-17].https://www.researchgate.net/publication/339768456_AirFaceLightweight_and_Efficient_Model_for_Face_Recognition.
[9] MARTÍNEZ-DÍAZ Y,LUEVANO L S,MÉNDEZ-VÁZQUEZ H,et al.ShuffleFaceNet:a lightweight face architecture for efficient and highly-accurate face recognition[EB/OL].[2021-11-17].https://ieeexplore.ieee.org/document/902220.
[10] YAN Mengjia,ZHAO Mengao,XU Zining,et al.VargFaceNet:an efficient variable group convolutional neural network for lightweight face recognition[EB/OL].[2021-11-17].https://ieeexplore.ieee.org/document/9022149/.
[11] LI Yuancheng,WANG Yimeng,LI Daoxing.Privacy-preserving lightweight face recognition[J].Neurocomputing,2019,363:212-222.
[12] HUANG Gao,LIU Shichen,VAL DER MAATEN L,et al.CondenseNet:an efficient DenseNet using learned group convolutions[EB/OL].[2021-11-17].https://arxiv.org/pdf/1711.09224.pdf.
[13] SUN Ke,LI Mingjie,LIU Dong,et al.IGCV3:interleaved low-rank group convolutions for efficient deep neural networks[EB/OL].[2021-11-17].https://arxiv.org/pdf/1806.00178.pdf.
[14] WU Bichen,ALVIN W,YUE Xiangyu,et al.Shift:a zero FLOP,zero parameter alternative to spatial convolutions[EB/OL].[2021-11-17].https://arxiv.org/pdf/1711.08141.pdf.
[15] HUANG G B,RAMESH M,BERG T,et al.Labeled faces in the wild:a database for studying face recognition in uncon-strained environments[EB/OL].[2021-11-17].http://cs.brown.edu/courses/cs143/2011/proj4/papers/lfw.pdf.
[16] CHOLLET F.Xception:deep learning with depthwise separable convolutions[EB/OL].[2021-11-17].https://arxiv.org/pdf/1610.02357.pdf.
[17] ZHANG Kaipeng,ZHANG Zhanpeng,LI Zhifeng,et al.Joint face detection and alignment using multitask cascaded convolutional networks[J].IEEE Signal Processing Letters,2016,23(10):1499-1503.
[18] VO D M,LEE S W.Robust face recognition via hierarchical collaborative representation[J].Information Sciences,2018,432:332-346.
[19] 杜星悦,董洪伟,杨振.基于深度网络的人脸区域分割方法[J].计算机工程与应用,2019,55(8):171-174.
[20] 王小玉,韩昌林,胡鑫豪.加权特征融合的密集连接网络人脸识别算法[J].计算机科学与探索,2019,13(7):1195-1205.
[21] DENG Jiankang,GUO Jia,XUE Niannan,et al.ArcFace:additive angular margin loss for deep face recognition[EB/OL].[2021-11-17].https://arxiv.org/pdf/1801.07698.pdf.
[22] DUONG C N,QUACH K G,JALATA I,et al.MobiFace:a lightweight deep learning face recognition on mobile devices[EB/OL].[2021-11-17].https://arxiv.org/pdf/1811.11080.pdf.
[23] WANG Xiaobo,FU Tianyuu,LIAO Shengcai,et al.Exclusivity-consistency regularized knowledge distillation for face recognition[EB/OL].[2021-11-17].https://link.springer.com/chapter/10.1007/978-3-030-58586-0_20.