Contactless Palmprint Recognition Based on the KLT Feature Points


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 11, pp. 495-502, Nov. 2014
10.3745/KTSDE.2014.3.11.495,   PDF Download:

Abstract

An effective solution to the variation on scale and rotation is required to recognize contactless palmprint. In this study, we firstlyminimize the variation by extracting a region of interest(ROI) according to the size and orientation of hand and normalizing the ROI. This paper proposes a contactless palmprint recognition method based on KLT(Kanade-Lukas-Tomasi) feature points. To detectcorresponding feature points, texture in local regions around KLT feature points are compared. Then, we recognize palmprint bymeasuring the similarity among displacement vectors which represent the size and direction of displacement of each pair ofcorresponding feature points. An experimental results using CASIA public database show that the proposed method is effective incontactless palmprint recognition. Especially, we can get the performance of exceeding 99% correct identification rate using multipleGabor filters.


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Cite this article
[IEEE Style]
M. K. Kim, "Contactless Palmprint Recognition Based on the KLT Feature Points," KIPS Transactions on Software and Data Engineering, vol. 3, no. 11, pp. 495-502, 2014. DOI: 10.3745/KTSDE.2014.3.11.495.

[ACM Style]
Min Ki Kim. 2014. Contactless Palmprint Recognition Based on the KLT Feature Points. KIPS Transactions on Software and Data Engineering, 3, 11, (2014), 495-502. DOI: 10.3745/KTSDE.2014.3.11.495.