Palmprint recognition system using VR-LBP and KAZE features for better recognition accuracy

Noor Aldeen A. Khalid, Muhammad Imran Ahmad, Tan Shie Chow, Thulfiqar H. Mandeel, Ibrahim Majid Mohammed, Mokhalad Abdulameer Kadhim Alsaeedi

Abstract


The palmprint recognition system has gained significant attention in security and law enforcement due to its unique features, such as principle lines, ridges, and wrinkles. However, many existing methods for extracting these features have limited accuracy, especially when the image illumination varies or the size of the processed pixels increases. Previous studies have shown that the local binary patterns (LBP) algorithm is effective for palmprint recognition due to the rich texture characteristics of a palmprint. In this paper, we propose a new technique for a robust contact-based palmprint identification system using vertical-LBP and KAZE feature detection. Our technique aims to improve recognition accuracy by using KAZE, which is a nonlinear diffusion approach that extracts nonlinear features from the evolution of the illuminance of an image. We also utilize principal component analysis (PCA) to reduce the dimensionality of the generated descriptor vector elements. The proposed method was tested on the PolyU database and achieved recognition accuracy of 99.7%.

Keywords


Euclidean distance; Palmprint recognition; Principal component analysis; Vertical-local binary patterns; KAZE

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DOI: https://doi.org/10.11591/eei.v13i2.4739

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).