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Facial emotion detection using Guided Particle Swarm Optimization (GPSO)

Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.

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Bibliographic Details
Main Authors: Bashir, Mohammed Ghandi, Nagarajan, R., Hazry, Desa
Other Authors: bmghandi@gmail.com
Format: Working Paper
Language:English
Published: Universiti Malaysia Perlis 2009
Subjects:
Emotion detection
Particle swarm optimization
PSO
Facial emotions
Facial expression
Facial action units
Swarm intelligence
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/7269
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spelling my.unimap-72692010-01-15T08:02:21Z Facial emotion detection using Guided Particle Swarm Optimization (GPSO) Bashir, Mohammed Ghandi Nagarajan, R. Hazry, Desa bmghandi@gmail.com Emotion detection Particle swarm optimization PSO Facial emotions Facial expression Facial action units Swarm intelligence Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia. In this paper, we present a novel approach to human facial emotion detection by applying a modified version of the Particle Swarm Optimization (PSO) algorithm, which we called Guided Particle Swarm Optimization (GPSO). Our approach is based on tracking the movements of facial action units (AUs) that are placed on the face of a subject and captured in video clips. We defined particles that form swarms as vectors consisting of points from each domain of the AUs considered. Particles are allowed to move around the effectively n-dimensional search space in search of the emotion being expressed in each frame of a video clip (where n is the number of action units being tracked). Since there are more than one possible target emotions at any point in time, multiple swarms are used, with each swarm having a specific emotion as its target. We have implemented and tested the algorithm on video clips that contain all the six basic emotions, namely happy, sad, surprise, disgust, anger and fear. Our results show the algorithm to have a promising success rate. 2009-11-13T01:57:08Z 2009-11-13T01:57:08Z 2009-10-11 Working Paper p.2A1 1 - 2A1 5 http://hdl.handle.net/123456789/7269 en Proceedings of the International Conference on Man-Machine Systems (ICoMMS 2009) Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Emotion detection
Particle swarm optimization
PSO
Facial emotions
Facial expression
Facial action units
Swarm intelligence
spellingShingle Emotion detection
Particle swarm optimization
PSO
Facial emotions
Facial expression
Facial action units
Swarm intelligence
Bashir, Mohammed Ghandi
Nagarajan, R.
Hazry, Desa
Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
description Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.
author2 bmghandi@gmail.com
author_facet bmghandi@gmail.com
Bashir, Mohammed Ghandi
Nagarajan, R.
Hazry, Desa
format Working Paper
author Bashir, Mohammed Ghandi
Nagarajan, R.
Hazry, Desa
author_sort Bashir, Mohammed Ghandi
title Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
title_short Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
title_full Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
title_fullStr Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
title_full_unstemmed Facial emotion detection using Guided Particle Swarm Optimization (GPSO)
title_sort facial emotion detection using guided particle swarm optimization (gpso)
publisher Universiti Malaysia Perlis
publishDate 2009
url http://dspace.unimap.edu.my/xmlui/handle/123456789/7269
_version_ 1643788743656603648
score 13.222552

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