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...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04027.pdf |
| _version_ | 1797775956662812672 |
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| author | Kumar H. Kishore Kumar S. Ashok |
| author_facet | Kumar H. Kishore Kumar S. Ashok |
| author_sort | Kumar H. Kishore |
| collection | DOAJ |
| description | 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<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting. |
| first_indexed | 2024-03-12T22:43:11Z |
| format | Article |
| id | doaj.art-53829107f77b40e9bce82582e1397d79 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T22:43:11Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-53829107f77b40e9bce82582e1397d792023-07-21T09:28:35ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013990402710.1051/e3sconf/202339904027e3sconf_iconnect2023_04027Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting AlgorithmKumar H. Kishore0Kumar S. Ashok1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityDepartment of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha UniversityThe 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<0.05). This suggests that the ResNet50 algorithm outperformed the X Gradient Boosting algorithm in this experiment. According to the study’s findings, ResNet50 is more effective in accurately identifying biometric palm prints compared to X Gradient Boosting.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04027.pdfbiometricfingerprintnovel resnet50palm printtechnologyxgradient boosting |
| spellingShingle | Kumar H. Kishore Kumar S. Ashok Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm E3S Web of Conferences biometric fingerprint novel resnet50 palm print technology xgradient boosting |
| title | Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm |
| title_full | Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm |
| title_fullStr | Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm |
| title_full_unstemmed | Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm |
| title_short | Accurate Biometric Palm Print Recognition Using ResNet50 algorithm Over X Gradient Boosting Algorithm |
| title_sort | accurate biometric palm print recognition using resnet50 algorithm over x gradient boosting algorithm |
| topic | biometric fingerprint novel resnet50 palm print technology xgradient boosting |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04027.pdf |
| work_keys_str_mv | AT kumarhkishore accuratebiometricpalmprintrecognitionusingresnet50algorithmoverxgradientboostingalgorithm AT kumarsashok accuratebiometricpalmprintrecognitionusingresnet50algorithmoverxgradientboostingalgorithm |