Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Chinese Optics Letters
  • Vol. 5,
  • Issue 3,
  • pp. 160-163
  • (2007)

Finger crease pattern recognition using Legendre moments and principal component analysis

Not Accessible

Your library or personal account may give you access

Abstract

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

© 2007 Chinese Optics Letters

PDF Article
More Like This
Temperature extraction in Brillouin optical time-domain analysis sensors using principal component analysis based pattern recognition

Abul Kalam Azad, Faisal Nadeem Khan, Waled Hussein Alarashi, Nan Guo, Alan Pak Tao Lau, and Chao Lu
Opt. Express 25(14) 16534-16549 (2017)

Finger vein verification system based on sparse representation

Yang Xin, Zhi Liu, Haixia Zhang, and Hong Zhang
Appl. Opt. 51(25) 6252-6258 (2012)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved