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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

邮发代号 80-970

2019 Impact Factor: 1.275

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Frontiers of Computer Science  2022, Vol. 16 Issue (5): 165332   https://doi.org/10.1007/s11704-021-0475-9
  本期目录
Endowing rotation invariance for 3D finger shape and vein verification
Hongbin XU1, Weili YANG2, Qiuxia WU1(), Wenxiong KANG2()
1. School of Software Engineering, South China University of Technology, Guangzhou 510006, China
2. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China
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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.
Key words： 3D finger-vein    biometrics    point-cloud    CNN
收稿日期: 2020-09-23      出版日期: 2022-01-28
Corresponding Author(s): Qiuxia WU,Wenxiong KANG   
 引用本文:   
. [J]. Frontiers of Computer Science, 2022, 16(5): 165332.
Hongbin XU, Weili YANG, Qiuxia WU, Wenxiong KANG. Endowing rotation invariance for 3D finger shape and vein verification. Front. Comput. Sci., 2022, 16(5): 165332.
 链接本文:  
https://academic.hep.com.cn/fcs/CN/10.1007/s11704-021-0475-9
https://academic.hep.com.cn/fcs/CN/Y2022/V16/I5/165332
Fig.1  
Fig.2  
Fig.3  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
Fig.8  
Subset Rotation Subjects Times Samples
SCUT-3DFV-V1-ER [8] Small 203 6 1218
SCUT-3DFV-V1-HR [8] Large 203 14 2842
3DPVFV-ER Small 702 10 7020
3DPVFV-HR Large 702 14 9828
Tab.1  
Methods [8] Ours
Device A1 A A B2
Implementation Matlab Matlab C++ C++
Acceleration √ √
Preprocessing 848.19 948.33 59.52 270.79
3D reconstruction 1532.09 724.65 4.75 19.94
Texture mapping 5331.14 328.04 28.49 138.5
Total 7711.42 2001.02 92.76 429.23
Tab.2  
Fig.9  
Fig.10  
Methods Data 3DFV-V1-ER/% 3DFV-V1-HR/%
DOCH [42] Image 8.42 25.56
Uniform LBP [43] Image 8.65 17.29
MCP [11] Image 4.32 20.75
Deepvein [20] Image 8.08 9.25
Das et al. [22] Image 6.25 8.77
Mobile CNN [8] Image 3.05 9.54
ResNet50 [44] Image 3.67 11.76
PointNet [9] Points 10.10 15.39
DensePoints [37] Points 4.11 7.73
DGCNN [36] Points 3.89 7.00
3DFVSNet Points 2.61 5.07
Tab.3  
Methods Data 3DPVFV-ER/% 3DPVFV-HR/%
DOCH [42] Image 18.05 31.78
Uniform LBP [43] Image 19.09 31.17
MCP [11] Image 14.44 28.40
Deepvein [20] Image 5.56 8.71
Das et al. [22] Image 5.67 8.91
Mobile CNN [8] Image 2.97 5.38
ResNet50 [44] Image 3.23 5.97
PointNet [9] Points 4.23 8.43
DensePoints [37] Points 4.88 8.36
DGCNN [36] Points 3.60 6.30
3DFVSNet Points 2.81 4.49
Tab.4  
Fig.11  
Fig.12  
Fig.13  
Fig.14  
Benchmark Modalities EER/%
3DFV-V1-ER Shape 4.81
Shape + Texture 2.61
3DFV-V1-HR Shape 12.15
Shape + Texture 5.07
3DPVFV-ER Shape 5.84
Shape + Texture 2.81
3DPVFV-HR Shape 10.00
Shape + Texture 4.49
Tab.5  
Method Groups 3DFV-V1-ER/% 3DFV-V1-HR/%
3DFVSNet 360 2.61 5.07
180 3.55 7.14
90 3.70 7.20
45 3.70 7.79
PointNet [9] 1 10.10 15.39
DensePoints [37] 1 4.11 7.73
DGCNN [36] 1 3.89 7.00
Tab.6  
Methods Groups 3DPVFV-ER/% 3DPVFV-HR/%
3DFVSNet 360 2.81 4.49
180 2.83 4.60
90 2.85 4.70
45 2.95 4.99
PointNet [9] 1 4.23 8.43
DensePoints [37] 1 4.88 8.36
DGCNN [36] 1 3.60 6.30
Tab.7  
Network Input Resolution Params FLOPs CPU runtime/ms GPU runtime/ms
3DFVSNet Points 40000 × 4 1.55M 3.41G 360 12.5
10000 × 4 891M 90 3.2
PointNet [9] Points 40000 × 4 0.83M 6.07G 570 15.7
10000 × 4 1.52G 150 6.3
DGCNN [36] Points 40000 × 4 1.82M 40.39G ? ?
10000 × 4 10.1G 5340 97.6
ResNet50 [44] Image 224 ×224 ×3 24.05M 4.11G 210 11.8
Deepvein [20] Image 128 ×128 ×1 70.63M 3.56G 220 8.7
Das et al. [22] Image 153 ×153 ×1 188.82M 19.32G 520 24.4
Tab.8  
Fig.15  
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