EFFECTIVE FEATURE EXTRACTION METHOD FOR UNCONSTRAINED ENVIRONMENT: LOCAL BINARY PATTERN OR LOCAL TERNARY PATTERN
DOI:
https://doi.org/10.59277/RRST-EE.2024.69.4.13Keywords:
Local binary pattern, Local ternary pattern, Feature extraction, Machine learning, Unconstrained environment, NoncooperationAbstract
In this study, a range of algorithms addressing the challenges posed by noise and illumination were investigated. Two algorithms, namely LTP and LBP, were selected for comparison due to their demonstrated effectiveness. The process becomes time-consuming due to training samples, mainly when dealing with images featuring higher levels of noise and illumination variations, necessitating efficient algorithms for effective recognition. To compare two effective feature extraction methods viz local binary pattern (LBP) and local ternary pattern (LTP) for an unconstraint environment. The impact of noise and illumination factors is particularly pronounced in the iris datasets of non-cooperative subjects, which serve as the input images for this analysis. These algorithms were applied to diverse datasets with distinctive illumination properties to facilitate feature extraction. The results indicated that the LTP exhibited efficiency in comparison, suggesting its efficacy in handling datasets with varying illumination characteristics. A comparative analysis between LBP and LTP was conducted on two distinct datasets, namely UBIRIS and CASIA. The investigation into the sensitivity of LTP revealed heightened sensitivity during the performance analysis test, with consistent accuracy observed at 50 samples and a scale of 0.3. In the case of the CASIA iris dataset, the recital of LTP and LBP exhibited nearly identical accuracy levels, converging after 70 samples for non-cooperative iris datasets compared to the LBP.
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