计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (2): 181-184.

• 图形图像处理 • 上一篇    下一篇

基于单样本的手掌静脉识别方法

林建民,冯  桂   

  1. 华侨大学 信息科学与工程学院,福建 厦门 361021
  • 出版日期:2015-01-15 发布日期:2015-01-12

Palm vein recognition method based on single sample

LIN Jianmin, FENG Gui   

  1. School of Information Science and Technology, Huaqiao University, Xiamen, Fujian 361021, China
  • Online:2015-01-15 Published:2015-01-12

摘要: 针对单样本手掌静脉识别率较低的问题,研究了一种结合手掌静脉2DPCA特征和分区LBP特征的识别方法。利用图像重采样和奇异值扰动方法生成虚拟样本,利用2DPCA从生成的虚拟样本图像上提取静脉特征进行识别;利用LBP从原单样本手掌静脉提取分区特征进行识别;利用决策层融合方法将以上两种方法进行融合。在PolyU手掌静脉库上的实验表明,该方法能有效地解决手掌静脉的单样本识别问题。

关键词: 静脉识别, 单样本, 虚拟图像, 二维主成分分析(2DPCA), 局部二值模式(LBP), 决策层融合

Abstract: In order to improve the low recognition rate of palm vein recognition algorithm based on a single sample, this paper presents a new algorithm-a combination 2DPCA feature and partition LBP feature. Image re-sampling and Singular Value Decomposition(SVD)-perturbation are used to generate virtual images, hence more samples. 2DPCA is used to extract the vein features from the generated virtual image. The original single sample palm vein image is partitioned into smaller sub-image. LBP is used to extract vein features for identification. The above two methods are integrated with the decision level fusion algorithm. Experimental results in PolyU palm vein database verify the effectiveness of this method.

Key words: vein recognition, single sample, virtual image, 2-Dimensional Principal Component Analysis(2DPCA), Local Binary Pattern(LBP), decision level fusion