ShuffaceNet： face recognition neural network based on ThetaMEX global pooling

doi:10.11772/j.issn.1001-9081.2022070985

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

ShuffaceNet： face recognition neural network based on ThetaMEX global pooling

Kansong CHEN, Yuan ZHENG, Lijun XU(), Zhouyu WANG, Zhe ZHANG, Fujuan YAO

School of Computer Science and Information Engineering，Hubei University，Wuhan Hubei 430062，China

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.
ZHENG Yuan， born in 2000， M. S. candidate. Her research interests include object detection， deep learning.
WANG Zhouyu， born in 1996， M. S. His research interests include object detection， artificial intelligence.
ZHANG Zhe， born in 1996， M. S. candidate. His research interests include machine learning， deep learning.
YAO Fujuan， born in 1999， M. S. candidate. Her research interests include digital twin， deep learning.
Supported by:
High Technology Key Program of Hubei Province(202011901203001);Key Research and Development Program of Hubei Province(2021BAA184);Knowledge Innovation Program of Wuhan-Shuguang Project(2022010801020327)

基于ThetaMEX全局池化的人脸识别神经网络——ShuffaceNet

陈侃松, 郑园, 许立君(), 王周宇, 张哲, 姚福娟

湖北大学计算机与信息工程学院，武汉 430062

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
郑园（2000—），女，湖南衡阳人，硕士研究生，主要研究方向：目标检测、深度学习
王周宇（1996—），男，湖北武汉人，硕士，主要研究方向：目标检测、人工智能
张哲（1996—），男，湖北荆州人，硕士研究生，主要研究方向：机器学习、深度学习
姚福娟（1999—），女，山东临沂人，硕士研究生，主要研究方向：数字孪生、深度学习。
基金资助:
湖北省科技重大专项(202011901203001);湖北省重点研发计划项目(2021BAA184);武汉市知识创新专项-曙光计划项目(2022010801020327)

Abstract

Abstract:

Focused on the issue that the current large-scale networks are not suitable to be applied on resource-starved mobile devices like smart phones and tablet computers， and the pooling layer will lead to the sparsity of feature map， which ultimately affect the recognition accuracy of the neural network， a lightweight face recognition neural network namely ShuffaceNet was proposed， a smooth nonlinear Log-Mean-Exp function ThetaMEX was designed， and an end-to-end trainable ThetaMEX Global Pool Layer （TGPL） was proposed， so as to reduce network parameters and improve computing speed while ensuring the accuracy of the algorithm， achieving the purpose that the network can be effectively deployed on mobile devices with limited resources. ShuffaceNet has about 3 600 parameters， and the model size is only 3.5 MB. The recognition test results on LFW （Labled Faces in the Wild）， AgeDB-30 （Age Database-30） and CFP （Celebrities in Frontal Profile） face datasets show that the accuracy of ShuffaceNet reaches 99.32%， 93.17%， 94.51% respectively. Compared with the traditional networks such as MobileNetV1， SqueezeNet and Xception， the proposed network has the size reduced by 73.1%， 82.1% and 78.5% respectively， and the accuracy on AgeDB-30 dataset improved by 5.0%， 6.3% and 6.7% respectively. It can be seen that the proposed network based on ThetaMEX global pooling can improve the model accuracy.

Key words: face recognition, smart global pooling, ThetaMEX, neural network, lightweight model

摘要：

针对目前大规模网络不适合在手机、平板电脑等资源匮乏的移动设备上使用，以及池化层会导致特征图的稀疏性最终影响神经网络识别精度的问题，提出了一个轻量级人脸识别神经网络ShuffaceNet，设计了一个非线性平滑Log-Mean-Exp函数ThetaMEX，并提出了一种端到端可训练的ThetaMEX全局池化层（TGPL），从而在保证算法精度的前提下，减少网络参数、提高运算速度，进而达到有效地将该网络部署在资源匮乏的移动设备上的目的。ShuffaceNet约有3 600个参数，模型大小仅为3.5 MB。在LFW（Labled Faces in the Wild）、AgeDB-30 （Age Database-30）、CFP （Celebrities in Frontal Profile）人脸数据集上的识别测试的结果表明，ShuffaceNet的精度分别达到了99.32%、93.17%、94.51%。与MobileNetV1、SqueezeNet、Xception相比，所提网络的大小分别缩减了73.1%、82.1%、78.5%，在AgeDB-30数据集上的精度分别提高了5.0%、6.3%、6.7%。可见，基于ThetaMEX全局池化的所提网络能够提高模型精度。

关键词: 人脸识别, 智能全局池化, ThetaMEX, 神经网络, 轻量级模型

CLC Number:

TP183

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.

Figures/Tables 15

References 20

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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
28×28×32	Shufface Stage1	64	1	2
14×14×144	Shufface Stage2	240	2	1
14×14×144	Shufface Stage2	240	1	4
7×7×288	Shufface Stage3	480	2	1
7×7×288	Shufface Stage3	480	1	4
5×5×960	Shufface Stage4（5×5）	960	2	1
5×5×960	Shufface Stage4（5×5）	960	1	2
1×1×960	ThetaMEX Pool
1×1×960	Fully Connect	1 000

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
28×28×32	Shufface Stage1	64	1	2
14×14×144	Shufface Stage2	240	2	1
14×14×144	Shufface Stage2	240	1	4
7×7×288	Shufface Stage3	480	2	1
7×7×288	Shufface Stage3	480	1	4
5×5×960	Shufface Stage4（5×5）	960	2	1
5×5×960	Shufface Stage4（5×5）	960	1	2
1×1×960	ThetaMEX Pool
1×1×960	Fully Connect	1 000

网络	精度/%				耗时/ms	参数量/10⁶
网络	LFW	AgeDB-30	cfp_fp	cfp_ff	耗时/ms	参数量/10⁶
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
网络	LFW	AgeDB-30	cfp_fp	cfp_ff	耗时/ms	参数量/10⁶
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
网络	LFW	AgeDB-30	cfp_fp	cfp_ff	耗时 /ms	参数量/10⁶
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

ShuffaceNet： face recognition neural network based on ThetaMEX global pooling

基于ThetaMEX全局池化的人脸识别神经网络——ShuffaceNet

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