中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold

文献类型:期刊论文

作者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
刊名Applied Sciences-Basel
出版日期2023
卷号13期号:10页码:21
DOI10.3390/app13105950
英文摘要In 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.
URL标识查看原文
语种英语
源URL[http://ir.ciomp.ac.cn/handle/181722/68282]  
专题中国科学院长春光学精密机械与物理研究所
推荐引用方式
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.

入库方式: OAI收割

来源:长春光学精密机械与物理研究所

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。