Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 595-600.DOI: 10.11772/j.issn.1001-9081.2021122214
• Multimedia computing and computer simulation • Previous Articles
Qi WANG(), Hang LEI, Xupeng WANG
Received:
2022-01-04
Revised:
2022-03-02
Accepted:
2022-03-21
Online:
2022-05-16
Published:
2023-02-10
Contact:
Qi WANG
About author:
LEI Hang, born in 1960, Ph. D., professor. His research interests include embedded operating system.Supported by:
通讯作者:
王奇
作者简介:
雷航(1960—),男,四川自贡人,教授,博士,CCF会员,主要研究方向:嵌入式操作系统基金资助:
CLC Number:
Qi WANG, Hang LEI, Xupeng WANG. Deep face verification under pose interference[J]. Journal of Computer Applications, 2023, 43(2): 595-600.
王奇, 雷航, 王旭鹏. 姿态干扰下的深度人脸验证[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 595-600.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122214
对象 | 第(1)组 | 第(2)组 | 第(3)组 | |||
---|---|---|---|---|---|---|
图像1 | 图像2 | 图像1 | 图像2 | 图像1 | 图像2 | |
对象1 | 0.62 | 0.62 | 0.51 | 0.52 | 0.42 | 0.43 |
对象2 | 0.50 | 0.50 | 0.51 | 0.51 | 0.53 | 0.53 |
对象3 | 0.37 | 0.37 | 0.33 | 0.33 | 0.39 | 0.39 |
Tab.1 Head pose and L2 norm
对象 | 第(1)组 | 第(2)组 | 第(3)组 | |||
---|---|---|---|---|---|---|
图像1 | 图像2 | 图像1 | 图像2 | 图像1 | 图像2 | |
对象1 | 0.62 | 0.62 | 0.51 | 0.52 | 0.42 | 0.43 |
对象2 | 0.50 | 0.50 | 0.51 | 0.51 | 0.53 | 0.53 |
对象3 | 0.37 | 0.37 | 0.33 | 0.33 | 0.39 | 0.39 |
概率阈值 | 准确率/% | 概率阈值 | 准确率/% |
---|---|---|---|
0.1 | 73.33 | 0.6 | 89.15 |
0.2 | 79.68 | 0.7 | 83.11 |
0.3 | 83.36 | 0.8 | 78.07 |
0.4 | 86.74 | 0.9 | 76.56 |
0.5 | 88.43 |
Tab. 2 Accuracy corresponding to different threshold value
概率阈值 | 准确率/% | 概率阈值 | 准确率/% |
---|---|---|---|
0.1 | 73.33 | 0.6 | 89.15 |
0.2 | 79.68 | 0.7 | 83.11 |
0.3 | 83.36 | 0.8 | 78.07 |
0.4 | 86.74 | 0.9 | 76.56 |
0.5 | 88.43 |
准确率/% | 准确率/% | ||
---|---|---|---|
0.2 | 87.62 | 10.0 | 89.98 |
0.4 | 88.77 | 12.0 | 89.91 |
0.6 | 89.26 | 14.0 | 89.89 |
0.8 | 89.58 | 16.0 | 89.68 |
1.0 | 89.74 | 18.0 | 89.63 |
2.0 | 89.70 | 20.0 | 89.25 |
4.0 | 89.72 | 24.0 | 88.08 |
6.0 | 89.66 | 28.0 | 88.12 |
8.0 | 89.81 | 不采用超球约束特征 | 89.15 |
Tab.3 Accuracy corresponding to different scaling parameters α
准确率/% | 准确率/% | ||
---|---|---|---|
0.2 | 87.62 | 10.0 | 89.98 |
0.4 | 88.77 | 12.0 | 89.91 |
0.6 | 89.26 | 14.0 | 89.89 |
0.8 | 89.58 | 16.0 | 89.68 |
1.0 | 89.74 | 18.0 | 89.63 |
2.0 | 89.70 | 20.0 | 89.25 |
4.0 | 89.72 | 24.0 | 88.08 |
6.0 | 89.66 | 28.0 | 88.12 |
8.0 | 89.81 | 不采用超球约束特征 | 89.15 |
方法 | 输入图像 | 模型 | GPU:1080ti | |||
---|---|---|---|---|---|---|
训练 | 测试 | 图像规格 | 模型参数/106 | 准确率/% | 速度/fps | |
JanusNet[ | RGB+Depth | Depth | 4.8 | 81.4 | 202 | |
Siamese[ | Depth | Depth | variable | 1.8 | 85.3 | 604 |
L2⁃Siamese | Depth | Depth | 56.0 | 89.9 | 148 |
Tab. 4 Comparison of experimental results of different methods on Pandora dataset
方法 | 输入图像 | 模型 | GPU:1080ti | |||
---|---|---|---|---|---|---|
训练 | 测试 | 图像规格 | 模型参数/106 | 准确率/% | 速度/fps | |
JanusNet[ | RGB+Depth | Depth | 4.8 | 81.4 | 202 | |
Siamese[ | Depth | Depth | variable | 1.8 | 85.3 | 604 |
L2⁃Siamese | Depth | Depth | 56.0 | 89.9 | 148 |
训练集 | 测试集 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
JanusNet[ | Siamese[ | L2-Siamese | ||||||||||
0.80 | 0.66 | 0.62 | 0.69 | 0.86 | 0.75 | 0.71 | 0.77 | 0.90 | 0.83 | 0.78 | 0.82 | |
0.83 | 0.79 | 0.77 | 0.80 | 0.87 | 0.84 | 0.81 | 0.85 | 0.90 | 0.88 | 0.87 | 0.89 | |
0.50 | 0.50 | 0.50 | 0.50 | 0.75 | 0.70 | 0.67 | 0.71 | 0.82 | 0.78 | 0.73 | 0.75 | |
0.80 | 0.75 | 0.72 | 0.76 | 0.88 | 0.84 | 0.81 | 0.85 | 0.91 | 0.88 | 0.87 | 0.90 |
Tab.5 Accuracy comparison of experimental results of different methods after grouping Pandora dataset according to poses
训练集 | 测试集 | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
JanusNet[ | Siamese[ | L2-Siamese | ||||||||||
0.80 | 0.66 | 0.62 | 0.69 | 0.86 | 0.75 | 0.71 | 0.77 | 0.90 | 0.83 | 0.78 | 0.82 | |
0.83 | 0.79 | 0.77 | 0.80 | 0.87 | 0.84 | 0.81 | 0.85 | 0.90 | 0.88 | 0.87 | 0.89 | |
0.50 | 0.50 | 0.50 | 0.50 | 0.75 | 0.70 | 0.67 | 0.71 | 0.82 | 0.78 | 0.73 | 0.75 | |
0.80 | 0.75 | 0.72 | 0.76 | 0.88 | 0.84 | 0.81 | 0.85 | 0.91 | 0.88 | 0.87 | 0.90 |
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