A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis - Computer and Computing Technologies in Agriculture IV - Part IV Access content directly
Conference Papers Year : 2011

A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis

Jia Wei Li
  • Function : Author
  • PersonId : 1013125
Ming Sun

Abstract

In recent years, as one of the biometric identification technology, palm-print identification has received many reseachers’ attention. To solve the key problem of palm-print recognition – feature extraction, we propose a new method, which based on wavelet transform and principal component analysis. In general, we use wavelet transform to deal with palm print images and extract high-dimensional wavelet energy features, then reduce the dimensionality of high-dimensional wavelet energy features through principal component analysis, and remain the original feature energy maximally. The features extracted by this method not only reflect palm-print images’ information maximally, but also achieve the goal of data dimensionality reduction. Experiments show, the correct recognition rates of new method are much higher than those traditional methods such as LDA [1], PCA [2], 2DPCA [3], ICA [4] and so on.
Fichier principal
Vignette du fichier
978-3-642-18369-0_5_Chapter.pdf (297.06 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01564880 , version 1 (19-07-2017)

Licence

Attribution

Identifiers

Cite

Jia Wei Li, Ming Sun. A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. pp.39-46, ⟨10.1007/978-3-642-18369-0_5⟩. ⟨hal-01564880⟩
52 View
154 Download

Altmetric

Share

Gmail Facebook X LinkedIn More