Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm

The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for e...

Full description

Saved in:
Bibliographic Details
Published in:E3S web of conferences 2023-01, Vol.399, p.4027
Main Authors: Kumar, H. Kishore, Kumar, S. Ashok
Format: Article
Language:English
Subjects:
Algorithms
biometric
Biometric recognition systems
Biometrics
fingerprint
novel resnet50
Palm
palm print
Statistical analysis
technology
xgradient boosting
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The aim of this research is to enhance the accuracy of biometric palm print identification by using the Novel ResNet50 Algorithm as compared to the X Gradient Boosting. Materials and Methods: In this study, the ResNet50 and X Gradient Boosting algorithms were compared using a sample size of 10 for each algorithm, resulting in a total sample size of 20. The comparison was carried out with a G Power of 0.8 and a confidence interval (CI) of 95% to ensure statistical significance. For this study the Birjand University Mobile Palmprint Database (BMPD) dataset was collected from the Kaggle repository, which includes a total of 1640 images containing both left and right-hand palmprints. Result: According to the results, the ResNet50 algorithm achieved a higher accuracy rate (94.7%) compared to the X Gradient Boosting algorithm (92.4%) in identifying and measuring the images. The statistical analysis indicated a significant difference between the Novel ResNet50 algorithm and X Gradient Boosting, with a pvalue of 0.003 (Independent sample T-test p
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202339904027