Open AccessAnalysis of local binary pattern using uniform bins as palm vein pattern descriptor
Author(s) -
Nurul Atikah Mohd Hayat,
Zarina Mohd Noh,
Norhidayah Mohamad Yatim,
Syafeeza Ahmad Radzi
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1502/1/012043
Subject(s) - local binary patterns , palm , pattern recognition (psychology) , artificial intelligence , python (programming language) , biometrics , palm print , computer science , classifier (uml) , histogram , image (mathematics) , physics , quantum mechanics , operating system
Palm vein authentication technology which reads the features of palm vein has been widely used in recent years as it offers high accuracy identification and difficult to be forged or impersonated. This paper demonstrates a palm vein recognition system using uniform Local Binary Pattern (LBP) through the Python language and R. Python language was used for contrast enhancement, noise reduction and LBP implementation while R was used for classifying palm vein pattern using K-Nearest Neighbour (KNN) classifier. The palm samples come from two datasets which are from the Chinese Academy of Sciences Institute of Automation (CASIA) and self-dataset. The outcomes were the extracted and classified palm vein pattern based on subjects and their accuracy based on each dataset. The accuracy for all uniform LBP bins and selected uniform LBP bins from self-dataset were 87% and 53% respectively; while for CASIA dataset were 60% and 27% respectively. The results show that the accuracy is higher if all uniform LBP bins are used for the recognition purpose.