Changchun Institute of Optics,Fine Mechanics and Physics,CAS
| 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 Publication | Applied Sciences-Basel
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| Volume | 13Issue:10Pages:21 |
| Abstract | 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. |
| DOI | 10.3390/app13105950 |
| URL | 查看原文 |
| Indexed By | sci |
| Language | 英语 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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. |
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| A 3D Occlusion Facia（11493KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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