International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 10 - Issue 10 |
Published: November 2010 |
Authors: Amel Bouchemha, Amine Nait-Ali, Nourredine Doghmane |
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Amel Bouchemha, Amine Nait-Ali, Nourredine Doghmane . Article:A Robust Technique to Characterize the Palmprint using Radon Transform and Delaunay Triangulation. International Journal of Computer Applications. 10, 10 (November 2010), 35-42. DOI=10.5120/1515-1895
@article{ 10.5120/1515-1895, author = { Amel Bouchemha,Amine Nait-Ali,Nourredine Doghmane }, title = { Article:A Robust Technique to Characterize the Palmprint using Radon Transform and Delaunay Triangulation }, journal = { International Journal of Computer Applications }, year = { 2010 }, volume = { 10 }, number = { 10 }, pages = { 35-42 }, doi = { 10.5120/1515-1895 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2010 %A Amel Bouchemha %A Amine Nait-Ali %A Nourredine Doghmane %T Article:A Robust Technique to Characterize the Palmprint using Radon Transform and Delaunay Triangulation%T %J International Journal of Computer Applications %V 10 %N 10 %P 35-42 %R 10.5120/1515-1895 %I Foundation of Computer Science (FCS), NY, USA
For the purpose of biometric applications, we explore in this paper a new robust approach to characterizing palmprint features. Instead of processing the acquired image in the spatial domain, the proposed technique extracts palmprint features using Radon transform and a geometric Delaunay triangulation jointly. In such a process, Radon transform enables the extraction of directional characteristics from the palm of the hand. Afterwards, the most significant information is structured using Delaunay triangulation, thus providing a specific palmprint signature. In order to compare the uniqueness as well as the stability of the palmprint signature, Hausdorff distance has been used as a criterion of similarity. As will be shown in this paper, the palmprint signature is very robust even when considering a low Signal-to-Noise Ratio (SNR). Promising results are obtained from a local database containing 200 palmprint images. This technique is mainly appropriate for authentication applications.