A Comparison of Principal Component Analysis and Adaptive Principal Component Extraction for Palmprint Recognition

Author

Ghandehari, Azadeh ; Safabakhsh, Reza

Author_Institution

Dept. of Eng. & Technol., Islamic Azad Univ., Saveh, Iran

fYear

2011

fDate

17-18 Nov. 2011

Firstpage

1

Lastpage

6

Abstract

This paper investigates palmprint recognition using Principal Component Analysis (PCA) and the Adaptive Principal component EXtraction (APEX) which is one of the PCA techniques involving neural network. Through implementing the PCA and APEX algorithms for extracting features and applying them to palmprint recognition with two classifiers, Euclidean distance and Hamming distance, it was made known that APEX algorithm is efficient in palmprint recognition and the rate of recognition given by APEX is way more than PCA.

Keywords

feature extraction; geometry; neural nets; palmprint recognition; principal component analysis; APEX; Euclidean distance classifier; Hamming distance classifier; PCA; adaptive principal component extraction; feature extraction; neural network; palmprint recognition; principal component analysis; Algorithm design and analysis; Covariance matrix; Eigenvalues and eigenfunctions; Feature extraction; Principal component analysis; Training; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Hand-Based Biometrics (ICHB), 2011 International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4577-0491-8

Electronic_ISBN

978-1-4577-0489-5

Type

conf

DOI

10.1109/ICHB.2011.6094307

Filename

6094307