Abstract
Face recognition using 3D point clouds is gaining growing interest, while raw point clouds often contain a significant amount of noise due to imperfect sensors. In this paper, an end-to-end 3D face recognition on a noisy point cloud is proposed, which synergistically integrates the denoising and recognition modules. Specifically, a Conditional Generative Adversarial Network on Three Orthogonal Planes (cGAN-TOP) is designed to effectively remove the noise in the point cloud, and recover the underlying features for subsequent recognition. A Linked Dynamic Graph Convolutional Neural Network (LDGCNN) is then adapted to recognize faces from the processed point cloud, which hierarchically links both the local point features and neighboring features of multiple scales. The proposed method is validated on the Bosphorus dataset. It significantly improves the recognition accuracy under all noise settings, with a maximum gain of 14.81%.
Abstract (translated)
基于三维点云的面部识别技术正日益受到关注，然而原始点云数据通常因传感器不完善而包含大量噪声。本文提出了一种针对含噪点云的端到端3D面部识别方法，该方法协同集成了去噪和识别模块。 具体而言，设计了一个基于三个正交平面的条件生成对抗网络（cGAN-TOP），以有效去除点云中的噪声，并恢复后续识别所需的底层特征。接着采用了一种链接动态图卷积神经网络（LDGCNN）来从处理后的点云中识别人脸，该方法层次化地连接了局部点特征及其多尺度邻域特征。 所提出的方法在博斯普鲁斯数据集上进行了验证，在所有噪声设置下均显著提高了识别准确率，最高提升了14.81%。
URL
https://arxiv.org/abs/2506.06864
