[1]QU Dongdong,HE Lile,HE Lin.Improved lightweight face recognition algorithm[J].CAAI Transactions on Intelligent Systems,2023,18(3):544-551.[doi:10.11992/tis.202111051]
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Improved lightweight face recognition algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 544-551 Column: 学术论文—智能系统 Public date: 2023-07-05
Title:
Improved lightweight face recognition algorithm
Author(s):
QU Dongdong1;  HE Lile1;  HE Lin2
1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China;
2. School of Science, Xi ’an University of Architecture and Technology, Xi’an 710055, China
Keywords:
embedded platform; deep learning; face recognition; lightweight network; mobilenet v2 model; softmax loss; Am-softmax loss; Jetson nano
CLC:
TP391.4
DOI:
10.11992/tis.202111051
Abstract:
Due to the limited computing resources, the embedded platform can not run the deep learning model with huge amounts of calculation and parameters in real time. An improved lightweight face recognition algorithm L-mobilenet v2 is proposed based on the mobilenet v2. The algorithm first optimizes the original network structure, then uses the triplet loss function as the main loss function to change the softmax loss in traditional classification task to Am-softmax, which is used as the auxiliary loss function, and uses 490 thousand images of 10575 people for joint training. Compared with the previous model and training method, the recognition accuracy of the new model on LFW test data set and self-made data set has reached 98.56% and 95%, respectively, which increased the recognition accuracy by 1.56 % and 7.1% while reducing the number of model parameters by 72.3%. And at the same time, the rate of frame recognition on average on the embedded platform Jetson nano has increased by 36.3%. The model can run in real time on mobile terminals with limited computing resources.
References:
[1] 李东博, 黄铝文. 重加权稀疏主成分分析算法及其在人脸识别中的应用[J]. 计算机应用, 2020, 40(3): 717–722
LI Dongbo, HUANG Lyuwen. Reweighted sparse principal component analysis algorithm and its application in face recognition[J]. Journal of computer applications, 2020, 40(3): 717–722
[2] 徐竟泽, 吴作宏, 徐岩, 等. 融合PCA、LDA和SVM算法的人脸识别[J]. 计算机工程与应用, 2019, 55(18): 34–37
XU Jingze, WU Zuohong, XU Yan, et al. Face recognition based on PCA, LDA and SVM algorithms[J]. Computer engineering and applications, 2019, 55(18): 34–37
[3] LIU Weiyang, WEN Yandong, YU Zhiding, et al. SphereFace: Deep Hypersphere Embedding for Face Recognition[C]//Proceedings of the 2017 IEEE conference on computer vision and pattern recognition. Piscataway: IEEE, 2017: 212?220.
[4] WANG Hao, WANG Yitong, ZHOU Zheng, et al. CosFace: large margin cosine loss for deep face recognition[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 5265?5274.
[5] DENG Jiankang, GUO Jia, YANG Jing, et al. Arcface: Additive angular margin loss for deep face recogniton[J]. IEEE transactions on pattern analysis and machine intelligence, 2021, 44(10): 5962–5979.
[6] SUN Yifan, CHENG Changmao, ZHANG Yuhan, et al. Circle loss: a unified perspective of pair similarity optimization[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 6397?6406.
[7] 才华, 孙俊, 朱瑞昆, 等. 基于自适应圆边际的深度人脸识别算法[J]. 兵工学报, 2021, 42(11): 2424–2432
CAI Hua, SUN Jun, ZHU Ruikun, et al. Depth face recognition algorithm based on adaptive circle margin[J]. Acta armamentarii, 2021, 42(11): 2424–2432
[8] HOWARD A G, ZHU MENGLONG, CHEN BO, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017?04?17)[2021?11?29]. https://arxiv.org/abs/1704.0486.
[9] ZHANG Xiangyu, ZHOU Xinyu, LIN Mengxiao, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[EB/OL]. (2017?07?04) [2021?11?29]. https://arxiv.org/abs/1707.01083.
[10] TAN Mingxing, LE Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. (2019?05?28) [2021?11?29]. https://arxiv.org/abs/1905.11946.
[11] SANDLER M, HOWARD A, ZHU Menglong, et al. MobileNetV2: inverted residuals and linear bottlenecks[EB/OL]. (2018?01?13)[2021?11?29]. https://arxiv.org/abs/1801.04381.
[12] GIRSHICK R. Fast R-CNN[C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2016: 1440?1448.
[13] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21?37.
[14] DENG Jiankang, GUO Jia, ZHOU Yuxian, et al. RetinaFace: single-stage dense face localisation in the wild[EB/OL]. (2019?03?02) [2021?11?29]. https://arxiv.org/abs/1905.00641.
[15] 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.
[16] SCHROFF F, KALENICHENKO D, PHILBIN J. FaceNet: a unified embedding for face recognition and clustering[EB/OL]. (2015?03?12)[2021?11?29].https://arxiv.org/abs/1503.03832.
[17] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[EB/OL]. (2015?12?10) [2021?11?29]. https://arxiv.org/abs/1512.03385.
[18] WEN Yandong, ZHANG Kaipeng, LI Zhifeng, et al. A discriminative feature learning approach for deep face recognition[C]//European Conference on Computer Vision. Cham: Springer, 2016: 499?515.
[19] WANG Feng, LIU Weiyang, LIU Haijun, et al. Additive margin softmax for face verification[EB/OL]. (2018?01?17) [2021?11?29]. https://arxiv.org/abs/1801.05599.
[20] YI Dong, LEI Zhen, LIAO Shengcai, et al. Learning face representation from scratch[EB/OL]. (2014?11?28) [2021?11?29].https://arxiv.org/abs/1411.7923.
[21] HUANG G B, LEARNED-MILLER E. Labeled faces in the wild: updates and new reporting procedures[R]. Amherst: University of Massachusetts, 2014.
[22] HUANG G B, MATTAR M, BERG T, et al. Labeled faces in the wild: a database forStudying face recognition in unconstrained environments[R]. Amherst: University of Massachusetts, 2007.
[23] KINGMA D P, BA J. Adam: a method for stochastic optimization[EB/OL]. (2014?12?22) [2021?11?29]. https://arxiv.org/abs/1412.6980.
[24] 张典, 汪海涛, 姜瑛, 等. 基于轻量级网络的实时人脸识别算法研究[J]. 计算机科学与探索, 2020, 14(2): 317–324
ZHANG Dian, WANG Haitao, JIANG Ying, et al. Research on real-time face recognition algorithm based on lightweight network[J]. Journal of frontiers of computer science and technology, 2020, 14(2): 317–324
[25] GUO Yandong, ZHANG Lei, HU Yuxiao, et al. MS-celeb-1M: a dataset and benchmark for large-scale face recognition[EB/OL]. (2016?07?27)[2021?11?29]. https://arxiv.org/abs/1607.08221.
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