Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors

With the ever-increasing popularity of social networks, a colossal amount of images are being uploaded to the digital world encompassing human faces. Analysis of such faces has led to the expansion of fascinating and enabling technologies in different spheres such as social sciences, entertainment,...

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Main Author:	Banaeeyan, Rasoul
Format:	Thesis
Published:	2018
Subjects:	Z665 Library Science Information Science
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id	my-mmu-ep.7720
record_format	uketd_dc
spelling	my-mmu-ep.77202020-09-17T05:39:54Z Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors 2018-10 Banaeeyan, Rasoul Z665 Library Science. Information Science With the ever-increasing popularity of social networks, a colossal amount of images are being uploaded to the digital world encompassing human faces. Analysis of such faces has led to the expansion of fascinating and enabling technologies in different spheres such as social sciences, entertainment, security, etc. Face retrieval is one instance of such enabling technologies which intends to locate index of one or more identical faces to a given query face. The performance of face retrieval systems heavily rely on the careful analysis of different facial components (eyes, nose, mouth, etc.) and, at a higher level, facial attributes (gender, race, age, hair color, eye color, etc.) owing to the fact that these semantic attributes help to tolerate some degrees of geometrical distortion, illuminations, expressions, and partial occlusions. However, solely employing facial attribute classiﬁers fail to add scalability in the context of thousands of distracting face images even though these classiﬁers are highly accurate. In addition, owing to the discriminative power of Convolutional Neural Networks (CNN) features, recent works have employed a complete set of deep transferred CNN features (taken from fully-connected layers) with a large feature dimensionality to obtain enhanced performance; yet these retrieval systems require high computational power and are very resource-demanding at the retrieval time due to the curse of dimensionality. Therefore, this study aims to exploit the distinctive capability of all facial attribute classiﬁers while their results are further reﬁned by a proposed sequential subset feature selection to reduce the dimensionality of the features extracted from a very deep pre-trained CNN model (VGG-face). 2018-10 Thesis http://shdl.mmu.edu.my/7720/ http://library.mmu.edu.my/library2/diglib/mmuetd/ phd doctoral Multimedia University Faculty of Engineering
institution	Multimedia University
collection	MMU Institutional Repository
topic	Z665 Library Science Information Science
spellingShingle	Z665 Library Science Information Science Banaeeyan, Rasoul Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
description	With the ever-increasing popularity of social networks, a colossal amount of images are being uploaded to the digital world encompassing human faces. Analysis of such faces has led to the expansion of fascinating and enabling technologies in different spheres such as social sciences, entertainment, security, etc. Face retrieval is one instance of such enabling technologies which intends to locate index of one or more identical faces to a given query face. The performance of face retrieval systems heavily rely on the careful analysis of different facial components (eyes, nose, mouth, etc.) and, at a higher level, facial attributes (gender, race, age, hair color, eye color, etc.) owing to the fact that these semantic attributes help to tolerate some degrees of geometrical distortion, illuminations, expressions, and partial occlusions. However, solely employing facial attribute classiﬁers fail to add scalability in the context of thousands of distracting face images even though these classiﬁers are highly accurate. In addition, owing to the discriminative power of Convolutional Neural Networks (CNN) features, recent works have employed a complete set of deep transferred CNN features (taken from fully-connected layers) with a large feature dimensionality to obtain enhanced performance; yet these retrieval systems require high computational power and are very resource-demanding at the retrieval time due to the curse of dimensionality. Therefore, this study aims to exploit the distinctive capability of all facial attribute classiﬁers while their results are further reﬁned by a proposed sequential subset feature selection to reduce the dimensionality of the features extracted from a very deep pre-trained CNN model (VGG-face).
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Banaeeyan, Rasoul
author_facet	Banaeeyan, Rasoul
author_sort	Banaeeyan, Rasoul
title	Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
title_short	Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
title_full	Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
title_fullStr	Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
title_full_unstemmed	Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors
title_sort	large-scale face image retrieval using semantic facial attributes and deep transferred descriptors
granting_institution	Multimedia University
granting_department	Faculty of Engineering
publishDate	2018
_version_	1747829666274607104

Large-Scale Face Image Retrieval Using Semantic Facial Attributes And Deep Transferred Descriptors

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