A Local Entropy Based Palmprint Image Enhancement Algorithm

This paper proposes a promising palmprint image enhancement algorithm. Under the constraint that keeps original characteristic of palmprint, the method improves contrast of images, in order to extract features from palmprint by Scale Invariance Feature Transformation (SIFT) descriptor. However, the...

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Bibliographic Details
Published in2009 First International Conference on Information Science and Engineering pp. 1043 - 1046
Main Authors Peng Liu, Xiao-Ming Ding, Di Liu, Dong-mei Sun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Biometrics
Data mining
Entropy
Feature extraction
Histograms
Image enhancement
Image recognition
Image resolution
Information science
Nonlinear filters
Online AccessGet full text
ISBN142444909X
9781424449095
ISSN2160-1283
DOI10.1109/ICISE.2009.53

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Summary:This paper proposes a promising palmprint image enhancement algorithm. Under the constraint that keeps original characteristic of palmprint, the method improves contrast of images, in order to extract features from palmprint by Scale Invariance Feature Transformation (SIFT) descriptor. However, the existing image enhancement algorithms can hardly yield SIFT feature from low resolution palmprint images, e.g., dpi of these images is less than 150. Our scheme uses local entropy to reassign the enhancement coefficients of traditional Unsharp Mask (UM) algorithm, for an enhancement of palmprint effectively. The algorithm solves the problem of obtaining SIFT keypoints from enhanced palmprint images successfully, which is failure by traditional UM based schemes or histogram equalization.
ISBN:142444909X
9781424449095
ISSN:2160-1283
DOI:10.1109/ICISE.2009.53