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16 October 2019 Face recognition on 3D point clouds
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Proceedings Volume 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019); 112051O (2019)
Event: Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 2019, Phuket, Thailand
Point cloud has achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, although image-based algorithm become more accurate and faster, open world face recognition still suffers from the influences i.e. illumination, occlusion, pose, etc. 3D face recognition based on point cloud containing both shape and texture information can compensate these shortcomings. However training a network to extract discriminative 3D feature is model complex and time inefficient due to the lack of large training dataset. To address these problems, we propose a novel 3D face recognition network(FPCNet) using modified PointNet++ and a 3D augmentation technique. Face-based loss and multi-label loss are used to train the FPCNet to enhance the learned features more discriminative. Moreover, a 3D face data augmentation method is proposed to synthesize more identity-variance and expression-variance 3D faces from limited data. Our proposed method shows excellent recognition results on CASIA-3D, Bosphorus and FRGC2.0 datasets and generalizes well for other datasets.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ziyu Zhang, Feipeng Da, Chenxing Wang, Jian Yu, and Yi Yu "Face recognition on 3D point clouds", Proc. SPIE 11205, Seventh International Conference on Optical and Photonic Engineering (icOPEN 2019), 112051O (16 October 2019);


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