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Two-phase palmprint identification

Two-phase palmprint identification In this paper, a two-phase palmprint recognition approach is proposed based on statistical features and wide principal line image features through dynamic region of interest (ROI). The ROI is segmented into overlapping segments by six schemes, and the statistical features are extracted directly from the segments. The algorithm focuses on the extraction of statistical features based on standard deviation and coefficient of variation. A modified dissimilarity distance is proposed for computing the distance between two palmprints. The procedures are presented for determining the size and location of the common region of training images dynamically. Experiments are conducted by using statistical features and the combination of statistical and wide principal line image features. The results show that the correct recognition rate (CRR) of the proposed approach is better than existing methods for PolyUPalmprint database. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Two-phase palmprint identification

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2020.110823
Publisher site
See Article on Publisher Site

Abstract

In this paper, a two-phase palmprint recognition approach is proposed based on statistical features and wide principal line image features through dynamic region of interest (ROI). The ROI is segmented into overlapping segments by six schemes, and the statistical features are extracted directly from the segments. The algorithm focuses on the extraction of statistical features based on standard deviation and coefficient of variation. A modified dissimilarity distance is proposed for computing the distance between two palmprints. The procedures are presented for determining the size and location of the common region of training images dynamically. Experiments are conducted by using statistical features and the combination of statistical and wide principal line image features. The results show that the correct recognition rate (CRR) of the proposed approach is better than existing methods for PolyUPalmprint database.

Journal

International Journal of Biometrics – Inderscience Publishers

Published: Jan 1, 2020

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