A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier

Jaafar, Haryati and Ibrahim, Salwani and Ramli, Dzati Athiar (2015) A Robust and Fast Computation Touchless Palm Print Recognition System Using LHEAT and the IFkNCN Classifier. Computational Intelligence and Neuroscience, 2015 (360217). pp. 1-17. ISSN 1687-5265

Download (6MB) | Preview


Mobile implementation is a current trend in biometric design.This paper proposes a new approach to palm print recognition, in which smart phones are used to capture palm print images at a distance.Atouchless systemwas developed because of public demand for privacy and sanitation. Robust hand tracking, image enhancement, and fast computation processing algorithms are required for effective touchless and mobile-based recognition. In this project, hand tracking and the region of interest (ROI) extractionmethod were discussed. A sliding neighborhood operation with local histogram equalization, followed by a local adaptive thresholding or LHEAT approach, was proposed in the image enhancement stage to manage low-quality palm print images. To accelerate the recognition process, a new classifier, improved fuzzy-based k nearest centroid neighbor (IFkNCN), was implemented. By removing outliers and reducing the amount of training data, this classifier exhibited faster computation. Our experimental results demonstrate that a touchless palm print system using LHEAT and IFkNCN achieves a promising recognition rate of 98.64%.

Item Type: Article
Subjects: T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Divisions: Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Article
Depositing User: Mr Noorazilan Noordin
Date Deposited: 03 Jan 2018 03:46
Last Modified: 03 Jan 2018 03:46
URI: http://eprints.usm.my/id/eprint/38193

Actions (login required)

View Item View Item