Title: Superpixel-based Zernike moments for palm-print recognition

Authors: Bilal Attallah; Amina Serir; Youssef Chahir; Abdelwahhab Boudjelal

Addresses: LASS Laboratory, Electronic Department, University of M'sila, Algiers, Algeria; LTIR Laboratory, Electronic Department, USTHB, Algiers, Algeria; Normandie University, UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen, France ' LTIR Laboratory, Electronic Department, USTHB, Algiers, Algeria ' Normandie University, UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen, France ' LASS Laboratory, Electronic Department, University of M'sila, Algiers, Algeria; Normandie University, UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen, France

Abstract: In the contemporary period, significant attention has been focused on the prospects of innovative personal recognition methods based on palm-print biometrics. However, diminished local consistency and interference from noise are only some of the obstacles that hinder the most common methods of palm-print imaging such as the grey texture and other low-level of the palm. Nevertheless, the development of the process and tackling of the obstacles faced have a potential solution in the form of high-level characteristic imaging for palm-print identification. In this study, Zernike moments are used for acquiring superpixel features that are spiral scanned images, which is an innovative recognition method. By using the extreme learning machine, the inter- and intra-similarities of the palm-print feature maps are determined. Our experiments yield good results with an accuracy rate of 97.52 and an equal error rate of 1.47% on the palm-print PolyU database.

Keywords: palm-print recognition; image segmentation; feature extraction; extreme learning machine; ELM; image matching.

DOI: 10.1504/IJESDF.2019.102561

International Journal of Electronic Security and Digital Forensics, 2019 Vol.11 No.4, pp.420 - 433

Received: 21 Sep 2017
Accepted: 11 Jul 2018
Published online: 30 Sep 2019 *

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