3D palmprint recognition by using local features and deep learning

被引：0
|
作者：
Yang B. [1 ]
Mo W.-B. [1 ]
Yao J.-L. [1 ]
机构：
[1] School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou
来源：
| 1600年 / Zhejiang University卷 / 54期
关键词：
3D palmprint; Curvature feature; Deep learning; Local geometric features; Shape index; Surface type;
D O I：
10.3785/j.issn.1008-973X.2020.03.014
中图分类号：
学科分类号：
摘要：
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. © 2020, Zhejiang University Press. All right reserved.
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页码：540 / 545
页数：5
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