基于深度学习的非接触掌纹识别方法

郑仕伟; 韩俊刚

Chinese

您当前的位置：

首页 >

文章列表页 >

基于深度学习的非接触掌纹识别方法

更新时间：2026-09-04

- 基于深度学习的非接触掌纹识别方法
- Deep Learning for Contact-less Palmprint Recognition
- 微电子学与计算机 2018年35卷第4期页码：98-102
- 作者机构：
  
  西安邮电大学机构计算机学院,陕西,西安,710121
- 作者简介：
- 基金信息：
  
  国家自然科学基金重点资助项目 61136002
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
移动端阅览
郑仕伟, 韩俊刚. 基于深度学习的非接触掌纹识别方法[J]. 微电子学与计算机, 2018, 35(4): 98-102.

ZHENG Shi-wei, HAN Jun-gang. Deep Learning for Contact-less Palmprint Recognition[J]. 2018, 35(4): 98-102.
郑仕伟, 韩俊刚. 基于深度学习的非接触掌纹识别方法[J]. 微电子学与计算机, 2018, 35(4): 98-102. DOI：

ZHENG Shi-wei, HAN Jun-gang. Deep Learning for Contact-less Palmprint Recognition[J]. 2018, 35(4): 98-102. DOI：

摘要

采用非接触的方式获取到的掌纹图像容易受到手掌摆放姿势、光照条件等因素的影响

造成识别效果欠佳.针对这些问题

提出使用卷积神经网络来处理非接触式掌纹识别问题.首先利用肤色阈值切割出手掌图像

采用一种指根点检测算法确定指根点

使用确定的指根点建立坐标系

分割出ROI图像.其次

使用卷积神经网络自动提取特征

并将提取到的特征送入分类器得出结果.最后

在公开掌纹数据库上进行实验

取得了不错的识别效果

证明了此方法的有效性.

Abstract

Under the influence of palm pose

illumination variation and complicated backgrounds; contact-less hand image recognition systems have unsatisfied recognition effect. To solve these question

a contact-less hand image recognition method based on convolutional neural network (CNN) was proposed. First the hand image was segmented from the background with a skin-color thresholding method.A finger base point detection method was used to find the finger base points. These points could be used to locate region of interest (ROI) of palm. Then CNN was used to extract hand features which could be basic for classification. The presented method was tested on publication hand image database. The results demonstrate that proposed algorithm can be used to improve the recognition accuracy.

关键词

Keywords

references

Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc, 2012: 1097-1105.

Hassanat A, Al-Awadi M, Btoush E, et al. New mobile phone and webcam hand images databases for personal authentication and identification[J]. Procedia Manufacturing, 2015, 3: 4060-4067. DOI: 4060

Badrinath G S, Gupta P, Mehrotra H. Score level fusion of voting strategy of geometric hashing and SURF for an efficient palmprint-based identification[J]. Journal of Real-Time Image Processing, 2013, 8(3): 265-284. DOI: 265

Jaafar H, Ibrahim S, Ramli D A. A robust and fast computation touchless palm print recognition system using LHEAT and the IFkNCN classifier[J]. Comput Intell Neurosci, 2015(5): 1-17. DOI: 1

Dian L, Dongmei S. Contactless palmprint recognition based on convolutional neural network[C]//Signal Processing (ICSP), 2016 IEEE 13th International Conference on. IEEE, 2016: 1363-1367.

Toshev A, Szegedy C. DeepPose: Human pose estimation via deep neural networks[C]// Computer Vision and Pattern Recognition. IEEE, 2014: 1653-1660.

Dongmei, Zhengding. A novel image enhancement method for SIFT feature extraction of low resolution palmprint images[J]. Chinese Journal of Electronics, 2011, 20(1): 111-113. DOI: 111

Frucci M, Nappi M, Riccio D, et al. WIRE: Watershed based iris recognition[J]. Pattern Recognition, 2016, 52: 148-159. DOI: 148

Rikhtegar A, Pooyan M, Manzuri-Shalmani M T. Genetic algorithm-optimised structure of convolutional neural network for face recognition applications[J]. Iet Computer Vision, 2016, 10(6): 559-566. DOI: 559

Kong A, Zhang D, Kamel M. A survey of palmprint recognition[J]. Pattern Recognition, 2009, 42(7): 1408-1418. DOI: 1408

岑瑶, 潘新, 郜晓晶, 等. 基于MB-LBP和HOG的掌纹识别[J]. 计算机应用研究, 2017, 34(3): 920-923. DOI: 920

Kumar A, Kwong C. Towards contactless, low-cost and accurate 3D fingerprint identification[J]. Pattern Analysis % Machine Intelligence IEEE Transactions on, 2015, 37(3): 681-696. DOI: 681

林森, 苑玮琦, 宋辉. 二进制鲁棒不变尺度特征在非接触掌纹识别中的应用[J]. 仪器仪表学报, 2013, 34(12): 2785-2792. DOI: 2785

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

结合经度加权的球形感知全景图像显著性预测算法

基于分块子图的CNN推理任务划分与存储调度算法

应用于锂电池SOC估计的PCNN_LSTM硬件加速器设计

基于U-Net网络结构的SAR图像去噪算法研究

轻量级(2+1)D卷积结构的动态手势识别研究