doi: 10.4304/jsw.9.3.531-535
Palmprint Recognition Using 2D-FLDA From a Single Image Per Person
- College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China
Abstract
Two dimensional Fisher linear discriminantanalysis(2D-FLDA) is a very effective method for palmprintrecognition. However, it cannot be used when each objecthas only one training sample because the within-class scattermatrices cannot be calculated. In this paper, a novel methodis developed to solve this problem. Using the blocksegmentation, wavelet transform, and sampling methods, anew training set containing three training samples in eachclass can be obtained. Then the 2D-FLDA can be applied toextract the discriminant palmprint feature vectors. Finallythe pattern classification can be implemented by the nearestneighbor classifier. Experimental results on the PolyUpalmprint database show that the proposed method isefficient and it has better recognition performance thanmany existing schemes.
Keywords
- single
- sample;
- Two
- dimensional
- Fisher
- li
How to Cite
Jinyu Guo, Haibin Chen, and Yuan Li, "Palmprint Recognition Using 2D-FLDA From a Single Image Per Person," Journal of Software, vol. 9, no. 3, pp. 531-535, 2014. https://doi.org/ 10.4304/jsw.9.3.531-535
Copyright & License
Copyright © 2014 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).