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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (3): 540-545    DOI: 10.3785/j.issn.1008-973X.2020.03.014
Computer Technology and Image Processing     
3D palmprint recognition by using local features and deep learning
Bing YANG(),Wen-bo MO,Jin-liang YAO
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China
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Abstract  

An efficient 3D palmprint recognition method was proposed by using local texture feature sets and deep learning, in order to explore the usage of 3D palmprint in biometrics recognition. Curvature feature, shape index and surface type were employed to describe the geometry characteristics of local regions in 3D palmprint data, and then take the charasteristics as the input of the deep neural network to finish 3D palmprint recognition task. Comprehensive experiments on Hong Kong Polytechnic University 3D palmprint database were further conducted by using different geometrical features and deep neural network models. The final experimental results of 3D palmprint recognition validate that the proposed method outperforms existing state-of-the-art methods in terms of recognition accuracy and runtime, showing high potential for real-time palmprint recognition applications.

Key words： 3D palmprint      local geometric features      curvature feature      shape index      surface type      deep learning     
Received: 28 February 2019      Published: 05 March 2020
CLC:  TP 391.4  
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Bing YANG
Wen-bo MO
Jin-liang YAO
Cite this article:

Bing YANG,Wen-bo MO,Jin-liang YAO. 3D palmprint recognition by using local features and deep learning. Journal of ZheJiang University (Engineering Science), 2020, 54(3): 540-545.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.03.014     OR     http://www.zjujournals.com/eng/Y2020/V54/I3/540


融合局部特征与深度学习的三维掌纹识别

为了探索三维掌纹在生物特征识别领域的应用，基于局部纹理特征和深度学习，提出一种有效的三维掌纹识别方法. 通过曲率特征、形状指数、表面类型分别来描述三维掌纹的局部几何特征，将其作为深度神经网络的输入，完成三维掌纹识别任务. 在香港理工大学的三维掌纹数据库上对不同的几何特征、不同的深度神经网络模型进行全面分析与比较. 三维掌纹识别实验结果表明，与其他三维掌纹识别方法相比较，所提方法的识别率更高，识别时间更短，在实时掌纹识别领域具有较大的应用潜力.


关键词： 三维掌纹,  局部几何特征,  曲率特征,  形状指数,  表面类型,  深度学习 
Fig.1 Framework of 3D palmprint recognition method
Fig.2 Mean curvature images and Gaussian curvature images of different 3D palms
Fig.3 Shape index images of different 3D palms
曲率 G> 0 G = 0 G< 0
M< 0 峰（ST = 1） 岭（ST = 2） 鞍岭（ST = 3）
M= 0 无（ST = 4） 平坦（ST = 5） 低点（ST = 6）
M> 0 坑（ST = 7） 谷（ST = 8） 鞍谷（ST = 9）
Tab.1 Three dimensional surface type definition
Fig.4 Surface type images of different 3D palms
Fig.5 Examples from Hong Kong Polytechnic University 3D palmprint database
三维特征 表面类型 平均曲率 高斯曲率
文献[5] 99.15 93.55 67.10
文献[15] 98.78 91.88 91.87
AlexNet模型 99.40 99.20 98.75
Tab.2 Identification accuracy by using different 3D palmprint recognition methods %
方法 N=1 N=2 N=4 N=10
ST+文献[5] 90.22 94.17 97.26 99.15
ST+ AlexNet 91.37 95.83 99.05 99.40
CBR[7] 95.11 97.31 99.52 99.66
Tab.3 Identification accuracy by using different training samples %
方法 tr 方法 tr
ST[15] 63 275.86 CBR[7] 858.29
MCI[15] 9 403.33 AlexNet+ST 96.562
GCI[15] 9 403.30 AlexNet+MCI 94.993
ST[5] 547.03 AlexNet+GCI 94.352
Tab.4 Runtime of different 3D palmprint recognition methods ms
三维特征 AlexNet GoogleNet Vgg16 ResNet50
表面类型 99.40 99.55 99.25 99.45
形状指数 99.30 99.00 97.25 98.90
平均曲率 99.20 99.15 99.55 98.80
高斯曲率 98.75 96.60 97.50 96.25
Tab.5 Identification accuracy by using different features and network structures of deep learning models %
三维特征 AlexNet GoogleNet Vgg16 ResNet50
表面类型 96.562 103.896 261.432 257.531
形状指数 95.865 103.812 261.175 256.421
平均曲率 94.993 103.461 259.813 254.594
高斯曲率 94.352 103.153 258.231 254.451
Tab.6 Runtime by using different features and network structuresof deep learning models ms
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