Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2030-2036.DOI: 10.11772/j.issn.1001-9081.2021050880
• Artificial intelligence • Previous Articles
Huaiqing HE, Jianqing YAN(), Kanghua HUI
Received:
2021-05-27
Revised:
2021-09-03
Accepted:
2021-09-15
Online:
2021-09-03
Published:
2022-07-10
Contact:
Jianqing YAN
About author:
HE Huaiqing, born in 1969, Ph. D., professor. Her research interests include graphics, image and visual analysis.Supported by:
通讯作者:
闫建青
作者简介:
贺怀清(1969—),女,吉林白山人,教授,博士,CCF会员,主要研究方向:图形、图像、可视化分析基金资助:
CLC Number:
Huaiqing HE, Jianqing YAN, Kanghua HUI. Lightweight face recognition method based on deep residual network[J]. Journal of Computer Applications, 2022, 42(7): 2030-2036.
贺怀清, 闫建青, 惠康华. 基于深度残差网络的轻量级人脸识别方法[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2030-2036.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021050880
层类型 | 输入尺寸 | 输出尺寸 |
---|---|---|
Conv1 | 3×112×112 | 64×112×112 |
Block1 | 64×112×112 | 64×56×56 |
Block2 | 64×56×56 | 128×28×28 |
Block3 | 128×28×28 | 256×14×14 |
Block4 | 256×14×14 | 512×7×7 |
FC | 512×7×7 | 1×25 088 |
Tab. 1 Each layer structure of lightweight face recognition residual network
层类型 | 输入尺寸 | 输出尺寸 |
---|---|---|
Conv1 | 3×112×112 | 64×112×112 |
Block1 | 64×112×112 | 64×56×56 |
Block2 | 64×56×56 | 128×28×28 |
Block3 | 128×28×28 | 256×14×14 |
Block4 | 256×14×14 | 512×7×7 |
FC | 512×7×7 | 1×25 088 |
模型 | 数据集精度/% | 单张识别 时间/ms | 空间开销/MB | |
---|---|---|---|---|
LFW | VGG-Face | |||
ResNet101 | 99.62 | 96.35 | 30 | 870.22 |
ResNet50 | 99.46 | 95.95 | 23 | 369.55 |
DSLR | 98.82 | 95.83 | 16 | 131.13 |
Tab. 2 Experimental results of teacher/student network on different datasets
模型 | 数据集精度/% | 单张识别 时间/ms | 空间开销/MB | |
---|---|---|---|---|
LFW | VGG-Face | |||
ResNet101 | 99.62 | 96.35 | 30 | 870.22 |
ResNet50 | 99.46 | 95.95 | 23 | 369.55 |
DSLR | 98.82 | 95.83 | 16 | 131.13 |
方法 | 数据集精度/% | 单张识别 时间/ms | |||
---|---|---|---|---|---|
LFW | VGG-Face | AgeDB | CFP-FP | ||
MobiFace | 98.60 | 95.70 | 92.32 | 92.83 | 15 |
HRNet | 99.40 | 95.98 | 93.10 | 93.65 | 20 |
GhostNet | 99.17 | 95.81 | 91.97 | 92.67 | 18 |
DSLR | 98.82 | 95.83 | 92.43 | 93.24 | 16 |
Tab. 3 Experimental results comparison of multiple methods on different datasets
方法 | 数据集精度/% | 单张识别 时间/ms | |||
---|---|---|---|---|---|
LFW | VGG-Face | AgeDB | CFP-FP | ||
MobiFace | 98.60 | 95.70 | 92.32 | 92.83 | 15 |
HRNet | 99.40 | 95.98 | 93.10 | 93.65 | 20 |
GhostNet | 99.17 | 95.81 | 91.97 | 92.67 | 18 |
DSLR | 98.82 | 95.83 | 92.43 | 93.24 | 16 |
方法 | 数据集精度/% | 单张识别 时间/ms | |
---|---|---|---|
LFW | VGG-Face | ||
IR | 98.85 | 95.91 | 18 |
IR+DSC | 98.79 | 95.74 | 16 |
IR+DSC+SE | 98.82 | 95.83 | 16 |
Tab. 4 Experimental results comparison of adding depthwise separable convolution to DSLR on different datasets
方法 | 数据集精度/% | 单张识别 时间/ms | |
---|---|---|---|
LFW | VGG-Face | ||
IR | 98.85 | 95.91 | 18 |
IR+DSC | 98.79 | 95.74 | 16 |
IR+DSC+SE | 98.82 | 95.83 | 16 |
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