Palm Print Recognition using Deep Learning
In recent decades, numerous studies have focused extensively on biometric palmprint recognition. Palm print recognition has gained significant popularity and importance across various domains owing to its exceptional efficiency and accuracy in personal identification. The biometric characterization...
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| Format: | Article |
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University North
2025-01-01
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| Series: | Tehnički Glasnik |
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| Online Access: | https://hrcak.srce.hr/file/480452 |
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| author | Ruaa Sadoon Salman Mauj Haider AbdAlkreem Qaswaa Khaled Abood |
| author_facet | Ruaa Sadoon Salman Mauj Haider AbdAlkreem Qaswaa Khaled Abood |
| author_sort | Ruaa Sadoon Salman |
| collection | DOAJ |
| description | In recent decades, numerous studies have focused extensively on biometric palmprint recognition. Palm print recognition has gained significant popularity and importance across various domains owing to its exceptional efficiency and accuracy in personal identification. The biometric characterization of a person's palm print is unique. However, a way to enhance the image is needed in order to produce a better and clearer image. Recently, palm print recognition methods based on features acquired using a series of convolutional neural networks have been introduced, among which DenseNet-121 has a densely connected structure, unlike other structures. This paper presents a scheme for palm print recognition by image enhancement. Contrast-limited adaptive histogram equation (CLAHE) is one of the image enhancement methods that can provide bounded segment and region size and is based on deep learning using DenseNet-121. To measure performance, the CASIA dataset was used. Experimental results on the DS show that the palm print features of Denes 21 achieve a recognition accuracy of 99 %, demonstrating the effectiveness and reliability of the proposed palm print. |
| format | Article |
| id | doaj-art-7a9164e23a514def883c49bad9f72b8a |
| institution | OA Journals |
| issn | 1846-6168 1848-5588 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University North |
| record_format | Article |
| series | Tehnički Glasnik |
| spelling | doaj-art-7a9164e23a514def883c49bad9f72b8a2025-08-20T02:06:20ZengUniversity NorthTehnički Glasnik1846-61681848-55882025-01-0119336837410.31803/tg-20240206125322Palm Print Recognition using Deep LearningRuaa Sadoon Salman0Mauj Haider AbdAlkreem1Qaswaa Khaled Abood2Ministry of Education, Karkh Three Directorate of Education, Baghdad, IraqMinistry of Education/Administrative Affairs, Baghdad, IraqUniversity of Baghdad, College of Science-Computer Science Department, Baghdad Governorate, Baghdad, IraqIn recent decades, numerous studies have focused extensively on biometric palmprint recognition. Palm print recognition has gained significant popularity and importance across various domains owing to its exceptional efficiency and accuracy in personal identification. The biometric characterization of a person's palm print is unique. However, a way to enhance the image is needed in order to produce a better and clearer image. Recently, palm print recognition methods based on features acquired using a series of convolutional neural networks have been introduced, among which DenseNet-121 has a densely connected structure, unlike other structures. This paper presents a scheme for palm print recognition by image enhancement. Contrast-limited adaptive histogram equation (CLAHE) is one of the image enhancement methods that can provide bounded segment and region size and is based on deep learning using DenseNet-121. To measure performance, the CASIA dataset was used. Experimental results on the DS show that the palm print features of Denes 21 achieve a recognition accuracy of 99 %, demonstrating the effectiveness and reliability of the proposed palm print.https://hrcak.srce.hr/file/480452biometricCLAHEdeep learningDenseNet-121ROI |
| spellingShingle | Ruaa Sadoon Salman Mauj Haider AbdAlkreem Qaswaa Khaled Abood Palm Print Recognition using Deep Learning Tehnički Glasnik biometric CLAHE deep learning DenseNet-121 ROI |
| title | Palm Print Recognition using Deep Learning |
| title_full | Palm Print Recognition using Deep Learning |
| title_fullStr | Palm Print Recognition using Deep Learning |
| title_full_unstemmed | Palm Print Recognition using Deep Learning |
| title_short | Palm Print Recognition using Deep Learning |
| title_sort | palm print recognition using deep learning |
| topic | biometric CLAHE deep learning DenseNet-121 ROI |
| url | https://hrcak.srce.hr/file/480452 |
| work_keys_str_mv | AT ruaasadoonsalman palmprintrecognitionusingdeeplearning AT maujhaiderabdalkreem palmprintrecognitionusingdeeplearning AT qaswaakhaledabood palmprintrecognitionusingdeeplearning |