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Endowing rotation invariance for 3D finger shape and vein verification

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Abstract

Finger vein biometrics have been extensively studied for the capability to detect aliveness, and the high security as intrinsic traits. However, vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition, especially in a contactless mode. To address the finger posture variation problem, we propose a 3D finger vein verification system extracting axial rotation invariant feature. An efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded platform. The main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based end-to-end neural network to extract deep axial rotation invariant feature, namely 3DFVSNet. In the network, the rotation problem is transformed to a permutation problem with the help of specially designed rotation groups. Finally, to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community, we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation, namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein (SCUT LFMB-3DPVFV) Dataset. Experimental results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61976095 and 61772225), the Guangdong Natural Science Foundation (2016A030313468 and 2020A1515010558), the Science and Technology Planning Project of Guangdong Province (2018B030323026), and the Fundamental Research Funds for the Central Universities (2018PY24).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Hongbin Xu & Qiuxia Wu

  2. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Weili Yang & Wenxiong Kang

Authors
  1. Hongbin Xu
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  2. Weili Yang
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  3. Qiuxia Wu
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  4. Wenxiong Kang
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Corresponding authors

Correspondence to Qiuxia Wu or Wenxiong Kang.

Additional information

Hongbin Xu received the BE degree from Nanchang University, China in 2018, and is currently working towards the MS degree at the South China University of Technology, China. His research interests include biometrics identification and 3D vision.

Weili Yang received the BE degree from Beijing University of Posts and Telecommunications, China in 2011, then received the MS degree from Wuhan University, China in 2013. He is a Lecturer in GuiZhou MinZu University, China and is currently pursuing the PhD degree at the South China University of Technology, China. His research interests include biometrics identification, computer vision, and deep learning.

Qiuxia Wu received her PhD degree in 2012 from the South China University of Technology, China. From October 2009 to October 2011, she was a Visiting Student with The University of Sydney, Australia. Since 2012, she had worked with the South China University of Technology, China as an Assistant Professor. From July 2012 to March 2016, she was with the Guangzhou Institute of Modern Industrial Technology, China and now she is an Associate Professor in the School of Software Engineering at the South China University of Technology, China. Her research interests include content based video retrieval, biometrics recognition, and biomedical.

Wenxiong Kang received the MS degree from Northwestern Polytechnical University, China in 2003, and the PhD degree from the South China University of Technology, China in 2009. He is currently a Professor with the School of Automation Science and Engineering, South China University of Technology, China. His research interests include biometrics identification, image processing, pattern recognition, and computer vision.

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Xu, H., Yang, W., Wu, Q. et al. Endowing rotation invariance for 3D finger shape and vein verification. Front. Comput. Sci. 16, 165332 (2022). https://doi.org/10.1007/s11704-021-0475-9

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