Obscure face recognition using deep neural networks
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| Język: | English |
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Brac University
2023
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| Dostęp online: | http://hdl.handle.net/10361/21936 |
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10361-219362023-12-07T21:02:27Z Obscure face recognition using deep neural networks Akash, MD Shahadat Hossain Sharife, Shadman Bin Datta, Bondon Huq, Aminul Reza, Tanzim Department of Computer Science and Engineering, Brac University Facial recognition Face detection Convolution Neural Networks Deep Neural Network InceptionResNetV1 ArcFace loss function SE-ResNeXt-101 Triplet loss function Facial expression. Artificial intelligence Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-40). One of the most effective biometric tools for securing diverse systems is facial recog- nition technology. Traditional security mechanisms like PINs, passwords, and fin- gerprints have shown to be less effective and dependable than this technology. Sys- tems for surveillance, finance, and security have all made substantial use of facial recognition technology. But facial recognition technology is currently facing a huge challenge from the COVID-19 pandemic. Due to face occlusion brought on by the widespread use of masks, it is now challenging to precisely identify people. This problem has motivated a number of academics to develop facial recognition tech- nology that is more accurate by focusing on hidden facial features. In this paper, we provide a sophisticated method for identifying faces even when there are facial alterations or masks are used. The CASIA, VGG2, LFW Databases are used to help us build our technique, which entails figuring out which facial traits are still discernible even when the face is partially hidden. Convolutional neural networks (CNNs), which are the foundation of our method and are used to extract perti- nent facial information, are based on deep learning techniques. We contrasted our findings with those of other cutting-edge facial recognition systems to assess our strategy. By obtaining more accuracy and speed, our system outperformed other systems. We got 95.85% accuracy in face recognition with the model Inception- ResNetV1 by using ArcFace loss function. Also got 94.28% accuracy on the same model by using Triplet loss function. We worked on the never before worked model for face recognition which is SE-ResNeXt-101. We also got 93.41% accuracy on that model with ArcFace loss function. That indicates that our research underlines the significance of creating environmental change-resistant facial recognition MD Shahadat Hossain Akash Shadman Bin Sharife Bondon Datta B.Sc. in Computer Science and Engineering 2023-12-07T06:27:15Z 2023-12-07T06:27:15Z 2023 2023-06 Thesis ID 19101440 ID 22241139 ID 19101143 http://hdl.handle.net/10361/21936 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. 40 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Facial recognition Face detection Convolution Neural Networks Deep Neural Network InceptionResNetV1 ArcFace loss function SE-ResNeXt-101 Triplet loss function Facial expression. Artificial intelligence Neural networks (Computer science) |
| spellingShingle |
Facial recognition Face detection Convolution Neural Networks Deep Neural Network InceptionResNetV1 ArcFace loss function SE-ResNeXt-101 Triplet loss function Facial expression. Artificial intelligence Neural networks (Computer science) Akash, MD Shahadat Hossain Sharife, Shadman Bin Datta, Bondon Obscure face recognition using deep neural networks |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Huq, Aminul |
| author_facet |
Huq, Aminul Akash, MD Shahadat Hossain Sharife, Shadman Bin Datta, Bondon |
| format |
Thesis |
| author |
Akash, MD Shahadat Hossain Sharife, Shadman Bin Datta, Bondon |
| author_sort |
Akash, MD Shahadat Hossain |
| title |
Obscure face recognition using deep neural networks |
| title_short |
Obscure face recognition using deep neural networks |
| title_full |
Obscure face recognition using deep neural networks |
| title_fullStr |
Obscure face recognition using deep neural networks |
| title_full_unstemmed |
Obscure face recognition using deep neural networks |
| title_sort |
obscure face recognition using deep neural networks |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21936 |
| work_keys_str_mv |
AT akashmdshahadathossain obscurefacerecognitionusingdeepneuralnetworks AT sharifeshadmanbin obscurefacerecognitionusingdeepneuralnetworks AT dattabondon obscurefacerecognitionusingdeepneuralnetworks |
| _version_ |
1814307950943010816 |