Face recognition on point cloud with cgan-top for denoising

2025/06/07 by Junyu Liu, Liu, Junyu, Jianfeng Ren +4
Computer Science · Engineering · #3D Shape Modeling and Analysis #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face and Expression Recognition #Face recognition and analysis

paper · pdf · doi:10.48550/arxiv.2506.06864

openalex publication_date 2025/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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%.

Citations

• Spatial-context-aware deep neural network for multi-class image classification
• Attention-based Dual-stream Vision Transformer for Radar Gait Recognition
• Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
• Feature Boosting Network For 3D Pose Estimation
• PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
• Patch-based Progressive 3D Point Set Upsampling
• SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection
• Dynamic Graph CNN for Learning on Point Clouds
• Dynamic Graph CNN for Learning on Point Clouds
• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• U-Net: Convolutional Networks for Biomedical Image Segmentation

Related

• Data-Free Point Cloud Network for 3D Face Recognition
• Few-Data Guided Learning Upon End-to-End Point Cloud Network for 3D Face Recognition
• Neural Generative Models for 3D Faces with Application in 3D Texture\n Free Face Recognition
• Learning Graph-Convolutional Representations for Point Cloud Denoising
• 3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation