Face recognition with accessories using CNN

This project is a study on how Convolutional Neural Networks (CNN) can be utilised to recognise faces occluded with accessories, specifically face masks. The widespread use of face masks, particularly highlighted by the COVID-19 pandemic, has significantly impaired the effectiveness of traditional f...

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Main Author: Muhammad Shafiq B Ninaba
Other Authors: Anamitra Makur
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
CNN
Online Access:https://hdl.handle.net/10356/176707
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1767072024-05-24T15:50:20Z Face recognition with accessories using CNN Muhammad Shafiq B Ninaba Anamitra Makur School of Electrical and Electronic Engineering EAMakur@ntu.edu.sg Computer and Information Science Engineering Face recognition CNN Convolutional neural network Neural network This project is a study on how Convolutional Neural Networks (CNN) can be utilised to recognise faces occluded with accessories, specifically face masks. The widespread use of face masks, particularly highlighted by the COVID-19 pandemic, has significantly impaired the effectiveness of traditional face recognition systems. To overcome this limitation, this study proposes an approach that trains a CNN model on a dataset of unoccluded faces, aiming to maintain high recognition accuracy even when faces are partially obscured. The project encompasses several phases, including the creation of a comprehensive face database, pre-processing of face images to facilitate optimal model training, and the development of a software-based recognition system implemented in Python. Special emphasis is placed on experimenting with data augmentation and hyperparameter tuning to improve model robustness. A key component of the project is the development of a web application, designed to demonstrate the model's capabilities. This application, encapsulated within Docker containers for ease of deployment, allows users to interact with the system by capturing and uploading images of their face for recognition, thereby showcasing the practical applications of the developed model. Preliminary results indicate that the CNN model achieves commendable accuracy in recognizing unoccluded faces. However, recognizing faces with accessories poses a greater challenge, highlighting the need for further model refinement. The project's findings contribute valuable insights into the capabilities and limitations of using CNNs for face recognition in the context of faces occluded with surgical masks, offering a foundation for future research and development in this critical area of biometric authentication. Bachelor's degree 2024-05-20T06:43:27Z 2024-05-20T06:43:27Z 2024 Final Year Project (FYP) Muhammad Shafiq B Ninaba (2024). Face recognition with accessories using CNN. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176707 https://hdl.handle.net/10356/176707 en A3010-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
Face recognition
CNN
Convolutional neural network
Neural network
spellingShingle Computer and Information Science
Engineering
Face recognition
CNN
Convolutional neural network
Neural network
Muhammad Shafiq B Ninaba
Face recognition with accessories using CNN
description This project is a study on how Convolutional Neural Networks (CNN) can be utilised to recognise faces occluded with accessories, specifically face masks. The widespread use of face masks, particularly highlighted by the COVID-19 pandemic, has significantly impaired the effectiveness of traditional face recognition systems. To overcome this limitation, this study proposes an approach that trains a CNN model on a dataset of unoccluded faces, aiming to maintain high recognition accuracy even when faces are partially obscured. The project encompasses several phases, including the creation of a comprehensive face database, pre-processing of face images to facilitate optimal model training, and the development of a software-based recognition system implemented in Python. Special emphasis is placed on experimenting with data augmentation and hyperparameter tuning to improve model robustness. A key component of the project is the development of a web application, designed to demonstrate the model's capabilities. This application, encapsulated within Docker containers for ease of deployment, allows users to interact with the system by capturing and uploading images of their face for recognition, thereby showcasing the practical applications of the developed model. Preliminary results indicate that the CNN model achieves commendable accuracy in recognizing unoccluded faces. However, recognizing faces with accessories poses a greater challenge, highlighting the need for further model refinement. The project's findings contribute valuable insights into the capabilities and limitations of using CNNs for face recognition in the context of faces occluded with surgical masks, offering a foundation for future research and development in this critical area of biometric authentication.
author2 Anamitra Makur
author_facet Anamitra Makur
Muhammad Shafiq B Ninaba
format Final Year Project
author Muhammad Shafiq B Ninaba
author_sort Muhammad Shafiq B Ninaba
title Face recognition with accessories using CNN
title_short Face recognition with accessories using CNN
title_full Face recognition with accessories using CNN
title_fullStr Face recognition with accessories using CNN
title_full_unstemmed Face recognition with accessories using CNN
title_sort face recognition with accessories using cnn
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176707
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