Biometric retina identification using artificial approach
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Materyal Türü: | Tez |
| Dil: | English |
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Brac University
2023
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| Online Erişim: | http://hdl.handle.net/10361/18004 |
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10361-180042023-03-22T21:01:48Z Biometric retina identification using artificial approach Imam, Syed Abrar Huda, Sheikh Samiul Alam, Talha Bin Ahsan, Anika Mostakim, Moin Khondaker, Arnisha Department of Computer Science and Engineering, Brac University CNN Retina Biometric Vessel Grey Scale Segmentation Augmentation Neural networks (Computer science) Artificial intelligence This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 16-18). In this paper, we considered recognizing 2D retina pictures with a Convolutional Neural Network (CNN) for greater accuracy since retina-based identification is the most secure way of establishing identity and identifying people. An artificial neural network that is used to examine pixel input and recognize and process images is called a CNN. CNN algorithm has been selected to identify 2D retina images because through the CNN algorithm faster and better accuracy can be achieved. The retina identification process includes gray scaling of the RGB retina images, vessel extraction of the retina in the 2D images and then data augmentation is performed to increase datasets. Our method was evaluated on 3 databases- ARIA, DRIVE and STARE and we achieved test accuracy of 1 multiple times within 45 epochs. Test accuracy of 0.983 is received as the highest average accuracy among every 10 epochs. The implementation of the identification process was done using the PyTorch package. Syed Abrar Imam Sheikh Samiul Huda Talha Bin Alam Anika Ahsan B. Computer Science 2023-03-22T07:14:57Z 2023-03-22T07:14:57Z 2022 2022-05 Thesis ID 18101153 ID 18101137 ID 18101168 ID 18101194 http://hdl.handle.net/10361/18004 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 18 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
CNN Retina Biometric Vessel Grey Scale Segmentation Augmentation Neural networks (Computer science) Artificial intelligence |
| spellingShingle |
CNN Retina Biometric Vessel Grey Scale Segmentation Augmentation Neural networks (Computer science) Artificial intelligence Imam, Syed Abrar Huda, Sheikh Samiul Alam, Talha Bin Ahsan, Anika Biometric retina identification using artificial approach |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Mostakim, Moin |
| author_facet |
Mostakim, Moin Imam, Syed Abrar Huda, Sheikh Samiul Alam, Talha Bin Ahsan, Anika |
| format |
Thesis |
| author |
Imam, Syed Abrar Huda, Sheikh Samiul Alam, Talha Bin Ahsan, Anika |
| author_sort |
Imam, Syed Abrar |
| title |
Biometric retina identification using artificial approach |
| title_short |
Biometric retina identification using artificial approach |
| title_full |
Biometric retina identification using artificial approach |
| title_fullStr |
Biometric retina identification using artificial approach |
| title_full_unstemmed |
Biometric retina identification using artificial approach |
| title_sort |
biometric retina identification using artificial approach |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/18004 |
| work_keys_str_mv |
AT imamsyedabrar biometricretinaidentificationusingartificialapproach AT hudasheikhsamiul biometricretinaidentificationusingartificialapproach AT alamtalhabin biometricretinaidentificationusingartificialapproach AT ahsananika biometricretinaidentificationusingartificialapproach |
| _version_ |
1814308824890212352 |