Automate recognition of facial expressions

Facial expression recognition (FER) has been a prominent research subject for decades. While learning human emotional purpose is a challenging activity that necessitates a huge amount of data, time, and complicated computations. Recently, with the shift of FER from laboratory-controlled to real-lif...

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Main Author: Zhang, Yuehan
Other Authors: Lu Shijian
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
Subjects:
Online Access:https://hdl.handle.net/10356/147928
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1479282021-04-16T06:45:33Z Automate recognition of facial expressions Zhang, Yuehan Lu Shijian School of Computer Science and Engineering Shijian.Lu@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Facial expression recognition (FER) has been a prominent research subject for decades. While learning human emotional purpose is a challenging activity that necessitates a huge amount of data, time, and complicated computations. Recently, with the shift of FER from laboratory-controlled to real-life applications and the recent progress of research in deep learning techniques, numerous studies in the automatic facial expression analysis field have been conducted due to its wide application. However, one of the major challenges in FER remains: performance is hampered by uncertainties in large-scale datasets obtained from unconstrained scenarios like FER2013. In this project, Automatic Recognition of Facial Expressions, we explore methods to improve FER system performance on large-scale unconstrained datasets. We firstly introduce existing novel deep neural networks and related training strategies. Then we try to reproduce some state-of-the-art classification results and discuss their advantages and limitations based on datasets and methods applied. After the exploration, we identify some constraints of these methods and try to figure out the cause, base on which, we propose our own ideas to address the constraints. Bachelor of Engineering (Computer Science) 2021-04-16T06:45:33Z 2021-04-16T06:45:33Z 2021 Final Year Project (FYP) Zhang, Y. (2021). Automate recognition of facial expressions. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/147928 https://hdl.handle.net/10356/147928 en SCSE20-0116 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 Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
spellingShingle Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Zhang, Yuehan
Automate recognition of facial expressions
description Facial expression recognition (FER) has been a prominent research subject for decades. While learning human emotional purpose is a challenging activity that necessitates a huge amount of data, time, and complicated computations. Recently, with the shift of FER from laboratory-controlled to real-life applications and the recent progress of research in deep learning techniques, numerous studies in the automatic facial expression analysis field have been conducted due to its wide application. However, one of the major challenges in FER remains: performance is hampered by uncertainties in large-scale datasets obtained from unconstrained scenarios like FER2013. In this project, Automatic Recognition of Facial Expressions, we explore methods to improve FER system performance on large-scale unconstrained datasets. We firstly introduce existing novel deep neural networks and related training strategies. Then we try to reproduce some state-of-the-art classification results and discuss their advantages and limitations based on datasets and methods applied. After the exploration, we identify some constraints of these methods and try to figure out the cause, base on which, we propose our own ideas to address the constraints.
author2 Lu Shijian
author_facet Lu Shijian
Zhang, Yuehan
format Final Year Project
author Zhang, Yuehan
author_sort Zhang, Yuehan
title Automate recognition of facial expressions
title_short Automate recognition of facial expressions
title_full Automate recognition of facial expressions
title_fullStr Automate recognition of facial expressions
title_full_unstemmed Automate recognition of facial expressions
title_sort automate recognition of facial expressions
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/147928
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