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

The aim of this research is to enhance the accuracy biometric palm print identification by using Novel ResNet50 Algorithm as compared X Gradient Boosting. Materials and Methods: In study, Boosting algorithms were a sample size 10 for each algorithm, resulting in total 20. comparison was carried out with G Power 0.8 confidence interval (CI) 95% ensure statistical significance. For study Birjand University Mobile Palmprint Database (BMPD) dataset collected from Kaggle repository, which includes 1640 images containing both left right-hand palmprints. Result: According results, algorithm achieved higher rate (94.7%) (92.4%) identifying measuring images. analysis indicated significant difference between Boosting, pvalue 0.003 (Independent T-test p<0.05). This suggests that outperformed experiment. study’s findings, more effective accurately prints