Palmprint identification using PalmCodes

Ajay Kumar Pathak; Helen C. Shen

doi:10.1109/ICIG.2004.110

Palmprint identification using PalmCodes

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

67 Citations (Scopus)

Abstract

This paper investigates a new approach for the palmprint identification using Real Gabor Function (RGF) filtering. Inkless composite hand images have been used to automatically extract the palmprints from peg-free imaging setup. These palmprints, after normalization, are subjected to selective feature sampling by a bank of RGF. Each of these filtered images has been used to extract significant features (PalmCode) from each of 6 concentric circular bands. Our preliminary experimental results using 400 low-resolution palmprint images achieve the recognition rate of 97.50% and also illustrate the shortcomings of results presented in earlier work. The results show the uniqueness of palmprint texture, even in the two hands of an individual and its possible use in biometrics based personal recognition.

Original language	English
Title of host publication	Proceedings - Third International Conference on Image and Graphics
Pages	258-261
Number of pages	4
DOIs	https://doi.org/10.1109/ICIG.2004.110
Publication status	Published - 1 Dec 2004
Event	Proceedings - Third International Conference on Image and Graphics - Hong Kong, Hong Kong Duration: 18 Dec 2004 → 20 Dec 2004

Conference

Conference	Proceedings - Third International Conference on Image and Graphics
Country/Territory	Hong Kong
City	Hong Kong
Period	18/12/04 → 20/12/04

ASJC Scopus subject areas

General Engineering

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10.1109/ICIG.2004.110

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title = "Palmprint identification using PalmCodes",

abstract = "This paper investigates a new approach for the palmprint identification using Real Gabor Function (RGF) filtering. Inkless composite hand images have been used to automatically extract the palmprints from peg-free imaging setup. These palmprints, after normalization, are subjected to selective feature sampling by a bank of RGF. Each of these filtered images has been used to extract significant features (PalmCode) from each of 6 concentric circular bands. Our preliminary experimental results using 400 low-resolution palmprint images achieve the recognition rate of 97.50% and also illustrate the shortcomings of results presented in earlier work. The results show the uniqueness of palmprint texture, even in the two hands of an individual and its possible use in biometrics based personal recognition.",

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AB - This paper investigates a new approach for the palmprint identification using Real Gabor Function (RGF) filtering. Inkless composite hand images have been used to automatically extract the palmprints from peg-free imaging setup. These palmprints, after normalization, are subjected to selective feature sampling by a bank of RGF. Each of these filtered images has been used to extract significant features (PalmCode) from each of 6 concentric circular bands. Our preliminary experimental results using 400 low-resolution palmprint images achieve the recognition rate of 97.50% and also illustrate the shortcomings of results presented in earlier work. The results show the uniqueness of palmprint texture, even in the two hands of an individual and its possible use in biometrics based personal recognition.

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BT - Proceedings - Third International Conference on Image and Graphics

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ER -

Palmprint identification using PalmCodes

Abstract

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