3D Face Recognition Based on Key Feature Enhancement Mechanism

被引：0
作者：
Wang Q. [1 ]
Qian W. [1 ,2 ]
Lei H. [1 ]
Wang X. [1 ]
机构：
[1] School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu
[2] Kash Institutec of Electronics and Information Industry, Kash
来源：
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2024年 / 53卷 / 02期
关键词：
3D face recognition; deep learning; feature enhancement; local feature descriptor; point cloud;
D O I：
10.12178/1001-0548.2023012
中图分类号：
学科分类号：
摘要：
3D face recognition is an important part of the field of computer vision. Pointnet relies on deep learning to solve the disorder of point clouds and realize the global feature extraction. However, due to the lack of detailed texture of point clouds, it is difficult to realize face recognition in complex situations only by global features. In deal with the above problems, a local feature descriptor is proposed to describe the local spatial geometric features of the point clouds, and a key feature enhancement mechanism is introduced to enhance the key features of the face through the probability distribution, which can reduce the interference of unnecessary features and effectively improve the accuracy of the model. Experiments were carried out on public data sets CASIA-3D, Lock3DFace and Bosphorus. The results show that our method can deal well with the change of expression, partial occlusion and interference of head pose, especially in weak light conditions, compared with RP-Net, the accuracy is improved by 1.1 percent, and the method also has good real-time performance. © 2024 University of Electronic Science and Technology of China. All rights reserved.
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页码：252 / 258
页数：6
相关论文
共 25 条
  • [1] BORGHI G, FABBRI M, VEZZANI R, Et al., Face-from-depth for head pose estimation on depth images, IEEE Transactions on Pattern Analysis & Machine Intelligence, 42, 3, pp. 596-609, (2018)
  • [2] QI C R, SU H, MO K, Et al., Pointnet: Deep learning on point sets for 3D classification and segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652-660, (2017)
  • [3] QI C R, YI L, SU H, Et al., Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5105-5114, (2017)
  • [4] DENG H, BIRDAL T, ILIC S., PPFNet: Global context aware local features for robust 3D point matching, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 195-205, (2018)
  • [5] LI Y, BU R, SUN M, Et al., PointCNN: Convolution on x-transformed
  • [6] XIE Z, CHEN J, PENG B., Point clouds learning with attention-based graph convolution networks, Neurocomputing, 402, pp. 245-255, (2020)
  • [7] ZHAO H, JIANG L, JIA J, Et al., Point transformer, Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 16259-16268, (2021)
  • [8] XIAO S H, SANG N, WANG X P., 3D point cloud head pose estimation based on deep learning, Journal of Computer Applications, 40, 4, pp. 996-1001, (2020)
  • [9] XIAO S H, SANG N, WANG X P, Et al., Leveraging ordinal regression with soft labels for 3D head pose estimation from point sets, ICASSP 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1883-1887, (2020)
  • [10] WANG Q, LEI H, WANG X., Deep face verification under posture interference, Journal of Computer Applications, 43, pp. 595-600, (2022)
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