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

• Main Authors: Jaafar, Haryati, Ibrahim, Salwani, Ramli, Dzati Athiar
• Format: Article
• Language: English
• Published: Hindawi Publishing Corporation 2015
• Subjects: TK1-9971 Electrical engineering. Electronics. Nuclear engineering
• Online Access: http://eprints.usm.my/38193/1/A_Robust_and_Fast_Computation_Touchless_Palm_Print_Recognition_System_Using.pdf; http://eprints.usm.my/38193/; http://dx.doi.org/10.1155/2015/360217

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%.