doi: 10.4304/jsw.9.2.287-292
Palmprint Recognition Based on Local Fisher Discriminant Analysis
- College of Information Engineering, Shenyang University of Chemical Technology, Shenyang, China
Abstract
A new palmprint recognition method based onlocal Fisher discriminant analysis(LFDA) is proposed. Inorder to solve the singularity of the eigenvalue equationmatrix in small-size-sample cases such as image recognition,image down-sample is first used to reduce the palmprintspace dimensionality. The LFDA is applied to extract thelow projection vectors. Then the training images and testimages are projected onto the projection vectors to get thelocal palmprint feature vectors. Finally, the cosine distancebetween two feature vectors is calculated to matchpalmprint. The new algorithm is tested in PolyU plmprintdatabase. The results show that compared with principalcomponent analysis (PCA), Fisher discriminant analysis(FDA), independent component analysis (ICA), and kernelprincipal component analysis (KPCA), the recognition rateof the new algorithm is the highest which is 98.95%, and therecognition time is 0.031s, so it meets the real-time systemspecification.
Keywords
- principal component analysis (PCA)
- Fish
How to Cite
Jinyu Guo, Haibin Chen, and Yuan Li, "Palmprint Recognition Based on Local Fisher Discriminant Analysis," Journal of Software, vol. 9, no. 2, pp. 287-292, 2014. https://doi.org/ 10.4304/jsw.9.2.287-292
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).