Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2572-2580.DOI: 10.11772/j.issn.1001-9081.2022070985
• Multimedia computing and computer simulation • Previous Articles
Kansong CHEN, Yuan ZHENG, Lijun XU(), Zhouyu WANG, Zhe ZHANG, Fujuan YAO
Received:
2022-07-08
Revised:
2022-11-16
Accepted:
2022-11-21
Online:
2023-01-15
Published:
2023-08-10
Contact:
Lijun XU
About author:
CHEN Kansong, born in 1972, Ph. D., professor. His research interests include artificial intelligence, digital twin, industrial internet.Supported by:
通讯作者:
许立君
作者简介:
陈侃松(1972—),男,湖北沙市人,教授,博士生导师,博士,主要研究方向:人工智能、数字孪生、工业互联网基金资助:
CLC Number:
Kansong CHEN, Yuan ZHENG, Lijun XU, Zhouyu WANG, Zhe ZHANG, Fujuan YAO. ShuffaceNet: face recognition neural network based on ThetaMEX global pooling[J]. Journal of Computer Applications, 2023, 43(8): 2572-2580.
陈侃松, 郑园, 许立君, 王周宇, 张哲, 姚福娟. 基于ThetaMEX全局池化的人脸识别神经网络——ShuffaceNet[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2572-2580.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022070985
输入 | 操作 | 通道数 | 步长 | 重复次数 |
---|---|---|---|---|
112×112×3 | Depthwise Conv3×3 | 32 | 1 | |
56×56×32 | Max Pool | 1 | 1 | |
28×28×32 | Shufface Stage1 | 64 | 2 | 1 |
64 | 1 | 2 | ||
14×14×144 | Shufface Stage2 | 240 | 2 | 1 |
240 | 1 | 4 | ||
7×7×288 | Shufface Stage3 | 480 | 2 | 1 |
480 | 1 | 4 | ||
5×5×960 | Shufface Stage4(5×5) | 960 | 2 | 1 |
960 | 1 | 2 | ||
1×1×960 | ThetaMEX Pool | |||
Fully Connect | 1 000 |
Tab. 1 ShuffaceNet network structure
输入 | 操作 | 通道数 | 步长 | 重复次数 |
---|---|---|---|---|
112×112×3 | Depthwise Conv3×3 | 32 | 1 | |
56×56×32 | Max Pool | 1 | 1 | |
28×28×32 | Shufface Stage1 | 64 | 2 | 1 |
64 | 1 | 2 | ||
14×14×144 | Shufface Stage2 | 240 | 2 | 1 |
240 | 1 | 4 | ||
7×7×288 | Shufface Stage3 | 480 | 2 | 1 |
480 | 1 | 4 | ||
5×5×960 | Shufface Stage4(5×5) | 960 | 2 | 1 |
960 | 1 | 2 | ||
1×1×960 | ThetaMEX Pool | |||
Fully Connect | 1 000 |
网络 | 精度/% | 耗时/ms | 参数量/106 | |||
---|---|---|---|---|---|---|
LFW | AgeDB-30 | cfp_fp | cfp_ff | |||
MobileNetV1 | 98.65 | 88.75 | 89.18 | 92.97 | 60 | 3.20 |
MobileNetV2 | 98.79 | 88.84 | 89.48 | 93.17 | 50 | 2.10 |
ShuffleNetV1 | 98.73 | 89.28 | 89.31 | 96.20 | 28 | 0.83 |
SqueezeNet | 98.53 | 87.61 | 87.78 | 94.67 | 30 | 4.80 |
Xception | 98.47 | 87.32 | 88.91 | 91.24 | 54 | 4.00 |
MobileFaceNet | 98.33 | 87.83 | 82.71 | 98.05 | 25 | 0.99 |
LightCNN-9 | 98.81 | 89.87 | 89.63 | 96.30 | 35 | 5.56 |
ShuffaceNet | 99.32 | 93.17 | 94.51 | 98.58 | 22 | 0.86 |
Tab. 2 Comparison of different network structures on different datasets
网络 | 精度/% | 耗时/ms | 参数量/106 | |||
---|---|---|---|---|---|---|
LFW | AgeDB-30 | cfp_fp | cfp_ff | |||
MobileNetV1 | 98.65 | 88.75 | 89.18 | 92.97 | 60 | 3.20 |
MobileNetV2 | 98.79 | 88.84 | 89.48 | 93.17 | 50 | 2.10 |
ShuffleNetV1 | 98.73 | 89.28 | 89.31 | 96.20 | 28 | 0.83 |
SqueezeNet | 98.53 | 87.61 | 87.78 | 94.67 | 30 | 4.80 |
Xception | 98.47 | 87.32 | 88.91 | 91.24 | 54 | 4.00 |
MobileFaceNet | 98.33 | 87.83 | 82.71 | 98.05 | 25 | 0.99 |
LightCNN-9 | 98.81 | 89.87 | 89.63 | 96.30 | 35 | 5.56 |
ShuffaceNet | 99.32 | 93.17 | 94.51 | 98.58 | 22 | 0.86 |
网络 | 精度/% | 耗时 /ms | 参数量/106 | |||
---|---|---|---|---|---|---|
LFW | AgeDB-30 | cfp_fp | cfp_ff | |||
MobileFaceNet | 98.33 | 87.83 | 82.71 | 98.05 | 28 | 0.99 |
Theta⁃MobileFaceNet | 98.51 | 88.21 | 83.53 | 98.40 | 28 | 1.00 |
ShuffleNetV1 | 98.73 | 89.28 | 83.53 | 98.40 | 25 | 0.83 |
Theta⁃Shufflefacenet | 97.79 | 88.01 | 83.10 | 98.24 | 25 | 1.00 |
ShuffaceNet | 99.32 | 93.17 | 94.51 | 98.58 | 23 | 0.86 |
Theta⁃ShuffaceNet | 98.96 | 94.52 | 95.31 | 99.10 | 22 | 0.86 |
Tab. 3 Comparison of accuracy and parameters of different models
网络 | 精度/% | 耗时 /ms | 参数量/106 | |||
---|---|---|---|---|---|---|
LFW | AgeDB-30 | cfp_fp | cfp_ff | |||
MobileFaceNet | 98.33 | 87.83 | 82.71 | 98.05 | 28 | 0.99 |
Theta⁃MobileFaceNet | 98.51 | 88.21 | 83.53 | 98.40 | 28 | 1.00 |
ShuffleNetV1 | 98.73 | 89.28 | 83.53 | 98.40 | 25 | 0.83 |
Theta⁃Shufflefacenet | 97.79 | 88.01 | 83.10 | 98.24 | 25 | 1.00 |
ShuffaceNet | 99.32 | 93.17 | 94.51 | 98.58 | 23 | 0.86 |
Theta⁃ShuffaceNet | 98.96 | 94.52 | 95.31 | 99.10 | 22 | 0.86 |
1 | 武文娟,李勇. Emfacenet:一种轻量级人脸识别的卷积神经网络[J]. 小型微型计算机系统, 2023, 44(3): 560-564. |
WU W J, LI Y. Emfacenet: a lightweight convolutional neural network for face recognition[J]. Journal of Chinese Computer Systems, 2023, 44(3): 560-564. | |
2 | LI X L, DING L K, WANG L, et al. FPGA accelerates deep residual learning for image recognition[C]// Proceedings of the IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference. Piscataway: IEEE, 2017: 837-840. 10.1109/itnec.2017.8284852 |
3 | ZHOU E J, CAO Z M, YIN Q. Naive-deep face recognition: touching the limit of LFW benchmark or not?[EB/OL]. (2015-01-20) [2022-10-26].. |
4 | WEN Y D, ZHANG K P, LI Z F, et al. A discriminative feature learning approach for deep face recognition[C]// Proceedings of 2016 European Conference on Computer Vision, LNCS 9911. Cham: Springer, 2016: 499-515. |
5 | LIU W Y, WEN Y D, YU Z D, 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: 6738-6746. 10.1109/cvpr.2017.713 |
6 | DENG J K, GUO J, XUE N N, et al. ArcFace: additive angular margin loss for deep face recognition[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 4685-4694. 10.1109/cvpr.2019.00482 |
7 | XIAO B, LI X Y, LI C G, et al. A novel pooling block for improving lightweight deep neural networks[J]. Pattern Recognition Letters, 2020, 135: 307-312. 10.1016/j.patrec.2020.05.012 |
8 | SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 4510-4520. 10.1109/cvpr.2018.00474 |
9 | ZHANG X Y, ZHOU X Y, LIN M X, et al. ShuffleNet: an extremely efficient convolutional neural network for mobile devices[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 6848-6856. 10.1109/cvpr.2018.00716 |
10 | CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 1800-1807. 10.1109/cvpr.2017.195 |
11 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 770-778. 10.1109/cvpr.2016.90 |
12 | IANDOLA F N, HAN S, MOSKEWICZ M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[EB/OL]. (2016-11-04) [2022-10-26].. |
13 | 赵锋,张鹏,张冉. 基于Ghostnet轻量级人脸识别算法研究[J]. 电子测量技术, 2022, 45(16):130-136. |
ZHAO F, ZHANG P, ZHANG R. Research on Ghostnet-based lightweight face recognition algorithm[J]. Electronic Measurement Technology, 2022, 45(16): 130-136. | |
14 | KATZ G, BARRETT C, DILL D L, et al. Reluplex: an efficient SMT solver for verifying deep neural networks[C]// Proceedings of the 2017 International Conference on Computer Aided Verification, LNCS 10426. Cham: Springer, 2017: 97-117. |
15 | COHEN N, SHARIR O, SHASHUA A. Deep SimNets[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 4782-4791. 10.1109/cvpr.2016.517 |
16 | ZHANG B X, ZHAO Q, FENG W Q, et al. AlphaMEX: a smarter global pooling method for convolutional neural networks[J]. Neurocomputing, 2018, 321: 36-48. 10.1016/j.neucom.2018.07.079 |
17 | CHEN S, LIU Y, GAO X, et al. MobileFaceNets: efficient CNNs for accurate real-time face verification on mobile devices[C]// Proceedings of the 2018 Chinese Conference on Biometric Recognition, LNCS 10996. Cham: Springer, 2018: 428-438. |
18 | LUO W J, LI Y J, URTASUN R, et al. Understanding the effective receptive field in deep convolutional neural networks[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2016: 4905-4913. |
19 | CAO Q, SHEN L, XIE W D, et al. VGGFace2: a dataset for recognising faces across pose and age[C]// Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition. Piscataway: IEEE, 2018:67-74. 10.1109/fg.2018.00020 |
20 | MOSCHOGLOU S, PAPAIOANNOU A, SAGONAS C, et al. AgeDB: the first manually collected, in-the-wild age database[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway: IEEE, 2017: 1997-2005. 10.1109/cvprw.2017.250 |
[1] | Xiaolin LI, Songjia YANG. Hybrid beamforming for multi-user mmWave relay networks using deep learning [J]. Journal of Computer Applications, 2023, 43(8): 2511-2516. |
[2] | Shengwei MA, Ruizhang HUANG, Lina REN, Chuan LIN. Structured deep text clustering model based on multi-layer semantic fusion [J]. Journal of Computer Applications, 2023, 43(8): 2364-2369. |
[3] | Gan LI, Mingdi NIU, Lu CHEN, Jing YANG, Tao YAN, Bin CHEN. Robotic grasp detection in low-light environment by incorporating visual feature enhancement mechanism [J]. Journal of Computer Applications, 2023, 43(8): 2564-2571. |
[4] | Hongjun HENG, Dingcheng YANG. Knowledge enhanced aspect word interactive graph neural network [J]. Journal of Computer Applications, 2023, 43(8): 2412-2419. |
[5] | Kun ZHANG, Fengyu YANG, Fa ZHONG, Guangdong ZENG, Shijian ZHOU. Source code vulnerability detection based on hybrid code representation [J]. Journal of Computer Applications, 2023, 43(8): 2517-2526. |
[6] | Zifang XIA, Yaxin YU, Ziteng WANG, Jiaqi QIAO. Explainable recommendation mechanism by fusion collaborative knowledge graph and counterfactual inference [J]. Journal of Computer Applications, 2023, 43(7): 2001-2009. |
[7] | Jiaming HE, Jucheng YANG, Chao WU, Xiaoning YAN, Nenghua XU. Person re-identification method based on multi-modal graph convolutional neural network [J]. Journal of Computer Applications, 2023, 43(7): 2182-2189. |
[8] | Yuanyuan QIN, Hong ZHANG. Pulmonary nodule detection algorithm based on attention feature pyramid networks [J]. Journal of Computer Applications, 2023, 43(7): 2311-2318. |
[9] | Pan YANG, Minqing ZHANG, Yu GE, Fuqiang DI, Yingnan ZHANG. Color image information hiding algorithm based on style transfer process [J]. Journal of Computer Applications, 2023, 43(6): 1730-1735. |
[10] | Huibin ZHANG, Liping FENG, Yaojun HAO, Yining WANG. Ancient mural dynasty identification based on attention mechanism and transfer learning [J]. Journal of Computer Applications, 2023, 43(6): 1826-1832. |
[11] | Zhixiong ZHENG, Jianhua LIU, Shuihua SUN, Ge XU, Honghui LIN. Aspect-based sentiment analysis model fused with multi-window local information [J]. Journal of Computer Applications, 2023, 43(6): 1796-1802. |
[12] | Hui WANG, Jianhong LI. Few-shot recognition method of 3D models based on Transformer [J]. Journal of Computer Applications, 2023, 43(6): 1750-1758. |
[13] | Rui XU, Shuang LIANG, Hang WAN, Yimin WEN, Shiming SHEN, Jian LI. Extraction of PM2.5 diffusion characteristics based on candlestick pattern matching [J]. Journal of Computer Applications, 2023, 43(5): 1394-1400. |
[14] | Jiahong SUI, Yingchi MAO, Huimin YU, Zicheng WANG, Ping PING. Global image captioning method based on graph attention network [J]. Journal of Computer Applications, 2023, 43(5): 1409-1415. |
[15] | Ruilin JIANG, Renchao QIN. Multi-neural network malicious code detection model based on depthwise separable convolution [J]. Journal of Computer Applications, 2023, 43(5): 1527-1533. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||