CIOMP OpenIR
A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold
K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu
2023
Source PublicationApplied Sciences-Basel
Volume13Issue:10Pages:21
AbstractIn this work, we propose a 3D occlusion facial recognition network based on a multi-feature combination threshold (MFCT-3DOFRNet). First, we design and extract the depth information of the 3D face point cloud, the elevation, and the azimuth angle of the normal vector as new 3D facially distinctive features, so as to improve the differentiation between 3D faces. Next, we propose a multi-feature combinatorial threshold that will be embedded at the input of the backbone network to implement the removal of occlusion features in each channel image. To enhance the feature extraction capability of the neural network for missing faces, we also introduce a missing face data generation method that enhances the training samples of the network. Finally, we use a Focal-ArcFace loss function to increase the inter-class decision boundaries and improve network performance during the training process. The experimental results show that the method has excellent recognition performance for unoccluded faces and also effectively improves the performance of 3D occlusion face recognition. The average Top-1 recognition rate of the proposed MFCT-3DOFRNet for the Bosphorus database is 99.52%, including 98.94% for occluded faces and 100% for unoccluded faces. For the UMB-DB dataset, the average Top-1 recognition rate is 95.08%, including 93.41% for occluded faces and 100% for unoccluded faces. These 3D face recognition experiments show that the proposed method essentially meets the requirements of high accuracy and good robustness.
DOI10.3390/app13105950
URL查看原文
Indexed Bysci
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ciomp.ac.cn/handle/181722/68282
Collection中国科学院长春光学精密机械与物理研究所
Recommended Citation
GB/T 7714
K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu. A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold[J]. Applied Sciences-Basel,2023,13(10):21.
APA K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu.(2023).A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold.Applied Sciences-Basel,13(10),21.
MLA K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu."A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold".Applied Sciences-Basel 13.10(2023):21.
Files in This Item: Download All
File Name/Size DocType Version Access License
A 3D Occlusion Facia(11493KB)期刊论文出版稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu]'s Articles
Baidu academic
Similar articles in Baidu academic
[K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[K. F. Zhu, X. He, Z. Lv, X. Zhang, R. D. Hao, X. He, J. Wang, J. W. He, L. Zhang and Z. Y. Mu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: A 3D Occlusion Facial Recognition Network Base.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.