A Study of Three Artificial Neural Networks Models' Ability to Identify Emotions from Facial Images

2010·5 MB·English

by  Hyde, Timothy Scott

Digitized by the Internet Archive
in 2012 with funding from
LYRASIS Members and Sloan Foundation
http://archive.org/details/studyofthreeartiOOhyde

Columbus State University
D. Abbott Turner College ofBusiness and Computer Science
School ofComputer Science
The Graduate Program in Applied Computer Science
A Study ofThree Artificial Neural Networks Models' Ability to Identify Emotions
from Facial Images
A
Thesis in
Applied Computer Science
by
Timothy Scott Hyde
Submitted in Partial Fulfillment
ofthe Requirements
for the Degree of
Master ofScience
May 2010
©2010 by Timothy Scott Hyde
I '
n*»tM-OQ rg
o ;-- m
r
rt" CCrnoseairC- l_i 7?
2 >2 3 -....:
DC32CP3CO i
«cc
COM D
CO
"» <J
PI CO
-3
M
* -3
-3 D1 !•;-
-;' :
2
M
M<J
-
iota
(£>• PI
;-'
i.
CTl (--!
fe -3
cn N..*
r-o
>
S O l-Tj H
~3 O 1—1 c
c r •
pi -3 -3
n : : ICTO* rq t- X
m z ?3
I—
; > 26-3 iDs31 CO ;px]i C> cc--3o o Da P>-]< 3>C!O" Cl
lO- rt PJ :r-
Q> C — rc«r
P3 CO I g £*"?'
CO pITi] o--] -3 rq
2 M 4
pa
cx> !--
1— S, &.
*1. OKO *~
o1
'*--»
CO
- o \
i- s
J -:
o1o 3 --
I
;. .; oh-»
C".
11
I have submitted this thesis in partial fulfillment ofthe requirements for the degree of
Master ofScience
Date Timothy Scott Hyde
We approve the thesis ofTimothy Scott Hyde as presented here.
^W
^/(L+fzoiO -—
Date Dr. Shamim Khan Associate Professor of
Computer Science, Thesis Advisor
£/<¥ f<2-+ 10
Date yp JonTrfhels, Associate Professor,
.
&
iccounting Finance
Date Dr. Edward Bosworth, Associate Professor
ofComputer Science

.
Ill
Abstract
Facial expressions conveying emotions are vital for human communication. They are also
important in the studies ofhuman interaction and behavioral studies. Recognition of
emotions, using facial images, may provide a fast and practical approach that is
noninvasive. Most previous studies ofemotion recognition through facial images were
based on the Facial Action Coding System (FACS). The FACS, which was developed by
Ekman and Freisen in 1978, was created to identify different facial muscular actions.
Previous artificial neural network-based approaches for classification offacial
expressions focused on improving one particular neural network model for better
accuracy. The purpose ofthis present study was to compare different artificial neural
network models, and determine which model was best at recognizing emotions through
facial images. The three neural network models were:
The Hopfield network
1
2. The Learning Vector Quantization network
3. Multilayer (Feedforward) back-propagation network
Several facial parameters were extracted from facial images and used in training the
different neural network models. The best performing neural network was the Hopfield
network at 72.50% accuracy. Next, the facial parameters were tested for their
significance in identifying facial expressions and a subset ofthe original facial
parameters was used to retrain the networks. The best performing network using the
subset offacial parameters was the LVQ network at 67.50% accuracy. This study has
helped to understand which neural network model was best at identifying facial