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Deep Learning Based Face Attributes Recognition

Face Recognition is a recently developing technology with numerous real life applications. The goal of this Final Year Project is to create a complete Face Attributes Recognition for security or facility. The automated face identification application is helpful in assisting forensic to survey an are...

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Main Author: Saidi, Mohamad Hazim
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2018
Subjects:
T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Online Access:http://eprints.usm.my/53327/1/Deep%20Learning%20Based%20Face%20Attributes%20Recognition_Mohamad%20Hazim%20Saidi_E3_2018.pdf
http://eprints.usm.my/53327/
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spelling my.usm.eprints.53327 http://eprints.usm.my/53327/ Deep Learning Based Face Attributes Recognition Saidi, Mohamad Hazim T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Face Recognition is a recently developing technology with numerous real life applications. The goal of this Final Year Project is to create a complete Face Attributes Recognition for security or facility. The automated face identification application is helpful in assisting forensic to survey an area with the implementation of Machine Learning (ML). It was once a difficult challenge due to uncertainties in the captured such as high variation of pose and obstruction corresponding to voluntary and involuntary factors. With the introduction of Deep Learning (DL), the concept of Convolutional Neural Network (CNN) that was once an idea can be realized. In this project, Fast CNN architecture is used as the core engine to power the face attributes recognition that aims to help users to identify its. The users can identify the face attributes including gender, glasses and facial hair. The face attributes recognition also can be performed well in identifying properly and improperly frontalized faces. Optimization is performed by experimenting in stages with several training parameters to obtain the best value for this unique purpose. Using this architecture, the best performance of training algorithm can be produced in order to recognize face attributes. Combined-algorithm based optimizers plays an important role in optimizing the training algorithm. The addition of convolutional layer is also essential in order to extract related facial features of facial images. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53327/1/Deep%20Learning%20Based%20Face%20Attributes%20Recognition_Mohamad%20Hazim%20Saidi_E3_2018.pdf Saidi, Mohamad Hazim (2018) Deep Learning Based Face Attributes Recognition. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
spellingShingle T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Saidi, Mohamad Hazim
Deep Learning Based Face Attributes Recognition
description Face Recognition is a recently developing technology with numerous real life applications. The goal of this Final Year Project is to create a complete Face Attributes Recognition for security or facility. The automated face identification application is helpful in assisting forensic to survey an area with the implementation of Machine Learning (ML). It was once a difficult challenge due to uncertainties in the captured such as high variation of pose and obstruction corresponding to voluntary and involuntary factors. With the introduction of Deep Learning (DL), the concept of Convolutional Neural Network (CNN) that was once an idea can be realized. In this project, Fast CNN architecture is used as the core engine to power the face attributes recognition that aims to help users to identify its. The users can identify the face attributes including gender, glasses and facial hair. The face attributes recognition also can be performed well in identifying properly and improperly frontalized faces. Optimization is performed by experimenting in stages with several training parameters to obtain the best value for this unique purpose. Using this architecture, the best performance of training algorithm can be produced in order to recognize face attributes. Combined-algorithm based optimizers plays an important role in optimizing the training algorithm. The addition of convolutional layer is also essential in order to extract related facial features of facial images.
format Monograph
author Saidi, Mohamad Hazim
author_facet Saidi, Mohamad Hazim
author_sort Saidi, Mohamad Hazim
title Deep Learning Based Face Attributes Recognition
title_short Deep Learning Based Face Attributes Recognition
title_full Deep Learning Based Face Attributes Recognition
title_fullStr Deep Learning Based Face Attributes Recognition
title_full_unstemmed Deep Learning Based Face Attributes Recognition
title_sort deep learning based face attributes recognition
publisher Universiti Sains Malaysia
publishDate 2018
url http://eprints.usm.my/53327/1/Deep%20Learning%20Based%20Face%20Attributes%20Recognition_Mohamad%20Hazim%20Saidi_E3_2018.pdf
http://eprints.usm.my/53327/
_version_ 1738511189441249280
score 13.211869

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