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Face recognition using deep learning

Face recognition system is a technology accomplished at verifying or identifying a person from a video frame from a video source or a digital image. Multiple processing layers have been applied by deep learning to learn representations of data with multiple levels of feature extraction, which have a...

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Bibliographic Details
Main Author: Ooi, Zi Xen
Format: Final Year Project / Dissertation / Thesis
Published: 2019
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utar.edu.my/3937/1/fyp_EE_2019_OZX.pdf
http://eprints.utar.edu.my/3937/
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id my-utar-eprints.3937
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spelling my-utar-eprints.39372021-01-08T08:09:21Z Face recognition using deep learning Ooi, Zi Xen T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Face recognition system is a technology accomplished at verifying or identifying a person from a video frame from a video source or a digital image. Multiple processing layers have been applied by deep learning to learn representations of data with multiple levels of feature extraction, which have achieved high accuracy to the real-world variations. Although the face recognition system has come a long way and its usage is crucial in several applications, it has remained a variety of challenges in face detection and recognition technologies, which including the pose variations, occlusions, facial expression changes, ageing of the face, illumination, etc. In this project, the real-time face recognition system is implemented using pretrained deep learning models with CCTV (Closed-Circuit Television) camera. The traditional CCTV is only good at recording and it is limited for campus safety and security nowadays. The face recognition system with CCTV camera can be used for controlling user access to physical locations of campus. The face recognition system does not require any kind of physical contact between the users and the device, which provide quick and convenient access to the authorized users. The developed face recognition system exploits the pre-trained Multi-task Cascaded Convolutional Network (MTCNN) model for face detection and the standard techniques with FaceNet embeddings as feature vectors for face recognition. The developed face recognition system was tested with numerous experiments to analyze its performance. Empirical results show the face recognition in uncontrolled environments is much more challenging than in controlled conditions. 2019-09-05 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3937/1/fyp_EE_2019_OZX.pdf Ooi, Zi Xen (2019) Face recognition using deep learning. Final Year Project, UTAR. http://eprints.utar.edu.my/3937/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Ooi, Zi Xen
Face recognition using deep learning
description Face recognition system is a technology accomplished at verifying or identifying a person from a video frame from a video source or a digital image. Multiple processing layers have been applied by deep learning to learn representations of data with multiple levels of feature extraction, which have achieved high accuracy to the real-world variations. Although the face recognition system has come a long way and its usage is crucial in several applications, it has remained a variety of challenges in face detection and recognition technologies, which including the pose variations, occlusions, facial expression changes, ageing of the face, illumination, etc. In this project, the real-time face recognition system is implemented using pretrained deep learning models with CCTV (Closed-Circuit Television) camera. The traditional CCTV is only good at recording and it is limited for campus safety and security nowadays. The face recognition system with CCTV camera can be used for controlling user access to physical locations of campus. The face recognition system does not require any kind of physical contact between the users and the device, which provide quick and convenient access to the authorized users. The developed face recognition system exploits the pre-trained Multi-task Cascaded Convolutional Network (MTCNN) model for face detection and the standard techniques with FaceNet embeddings as feature vectors for face recognition. The developed face recognition system was tested with numerous experiments to analyze its performance. Empirical results show the face recognition in uncontrolled environments is much more challenging than in controlled conditions.
format Final Year Project / Dissertation / Thesis
author Ooi, Zi Xen
author_facet Ooi, Zi Xen
author_sort Ooi, Zi Xen
title Face recognition using deep learning
title_short Face recognition using deep learning
title_full Face recognition using deep learning
title_fullStr Face recognition using deep learning
title_full_unstemmed Face recognition using deep learning
title_sort face recognition using deep learning
publishDate 2019
url http://eprints.utar.edu.my/3937/1/fyp_EE_2019_OZX.pdf
http://eprints.utar.edu.my/3937/
_version_ 1690375313674993664
score 13.211869

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