Endowing rotation invariance for 3D finger shape and vein verification

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.

Graphical abstract

Keywords

3D finger-vein / biometrics / point-cloud / CNN

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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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