A 3D Occlusion Facial Recognition Network Based on a Multi-Feature Combination Threshold

doi:10.3390/app13105950

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	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
发表期刊	Applied Sciences-Basel
卷号	13 期号:10 页码:21
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.
DOI	10.3390/app13105950
URL	查看原文
收录类别	sci
语种	英语
引用统计
文献类型	期刊论文
条目标识符	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.

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