Face recognition on point cloud with cgan-top for denoising

• URL: http://arxiv.org/abs/2506.06864v1
• Date: Sat, 07 Jun 2025 17:09:31 GMT
• Title: Face recognition on point cloud with cgan-top for denoising
• Authors: Junyu Liu, Jianfeng Ren, Sunhong Liang, Xudong Jiang,
• Abstract summary: An end-to-end 3D face recognition on a noisy point cloud is proposed.<n> Conditional Generative Adrial Network on Three Orthogonal Planes (cGAN-TOP) is designed to remove the noise in the point cloud.<n>A Linked Dynamic Graph Convolutional Neural Network (LDGCNN) is then adapted to recognize faces from the processed point cloud.
• Score: 28.209519473639627
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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%.

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