TY - GEN
T1 - Face Recognition on Point Cloud with Cgan-Top for Denoising
AU - Liu, Junyu
AU - Ren, Jianfeng
AU - Sun, Hongliang
AU - Jiang, Xudong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/5/5
Y1 - 2023/5/5
N2 - 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%.
AB - 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%.
KW - 3D Point Cloud
KW - Conditional Generative Neural Network on Three Orthogonal Planes
KW - Face Recognition
KW - Linked Dynamic Graph Convolutional Neural Network
UR - http://www.scopus.com/inward/record.url?scp=86000376719&partnerID=8YFLogxK
U2 - 10.1109/ICASSP49357.2023.10096203
DO - 10.1109/ICASSP49357.2023.10096203
M3 - Conference contribution
AN - SCOPUS:86000376719
SN - 9781728163284
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1
EP - 5
BT - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Y2 - 4 June 2023 through 10 June 2023
ER -
