The Quantification of Human Facial Expression Using Fuzzy Logic

The Quantification of Human facial expression Using Fuzzy
Logic.
Dileep M R 1
and Ajit Danti 2
N E S Research Foundation,
Department of Computer Applications,
Jawaharlal Nehru National College of Engineering, Shimoga, Karnataka, India
1
dileep.kurunimakki@gmail.com
2
ajitdanti@yahoo.com
Abstract: Fuzzy logic is an interesting theory that allows the natural description, in linguistic terms, of
problems that should be solved rather than in terms of relationships between precise numerical values. In
this paper, an effective approach is proposed that quantifies the human facial expression using Mamdani
implication based fuzzy logic system. The new technique involves in extracting mathematical data from
the face and fed to a fuzzy rule-based system. Fuzzification and Defuzzification operation issues
trapezoidal membership functions for both input and output. The distinct feature of a system is its
simplicity and high accuracy. Experimental results on Image dataset indicate good performance of the
proposed technique. Comparative analysis reveal that the proposed technique is uniqueness and robust
with reference to other state of the art methods.
In this paper, a legitimate procedure proposed for quantification of human facial expression
recognition from Facial features using Mamdani-type fuzzy system. It is Fuzzy Inference System (FIS),
which is capable to set up an easy membership relation between the different dimensions of the happy
expression. The FIS recognizes three levels of same happy expression namely No happy, Bit Smiley and
Loud Laugh based on membership function modeled on different psychological studies and surveys.
Index Terms – Fuzzy Rule, Quantification of Expression, Membership Function
1. INTRODUCTION
Facial expression is one of the most important subjects in the field of biometric,
which has wide range of applications such as Business, Managerial, Organizational, Cultural
contexts, Telecommunication, Medical, Human Computer Interactions (HCI). The ideal human
computer interaction system is the one that the computer is able to communicate and respond to
the user actions, based on emotional state of human's face. In this way the user will be able to
communicate with it more effectively. For this aim, automatic recognition of human's facial
expressions has been very active research area in machine vision within the last several years.
Facial expressions are generated by movement of face muscles that makes facial features such as
stretching corner lips, raising eyebrows, opening eyes, etc.
The traditional approach to building any system controllers requires a prior model of the
system. The quality of the model, that is, loss of precision from linearization and/or uncertainties
in the system’s parameters negatively influences the quality of the resulting control. At the same
time, methods of soft computing such as fuzzy logic possess non-linear mapping capabilities, do
not require an analytical model and can deal with uncertainties in the system’s parameters.
Although fuzzy logic deals with imprecise information, the information is processed in sound
mathematical theory. Based on the nature of fuzzy human thinking, Zadeh, originated the “fuzzy
logic” or “fuzzy set theory”, in 1965. Fuzzy logic deals with the problems that have fuzziness or
vagueness. In fuzzy set theory based on fuzzy logic a particular object has a degree of
membership in a given set that may be anywhere in the range of 0 (completely not in the set) to 1
International Journal of Computer Science and Information Security (IJCSIS),
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119 https://sites.google.com/site/ijcsis/
ISSN 1947-5500

(completely in the set). For this reason fuzzy logic is often defined as multi-valued logic (0 to 1),
compared to bi-valued Boolean logic.
Facial expression process usually extract facial expression parameters from a static face
image. This process is called as “Quantification” of expression. These extracted features are
then fed to a classifier system for facial expression quantification by defining the range of
expression with their membership function. In this paper, a complete system for quantification of
facial expression i,e different range of happy faces is proposed. The core of our system is a
Mamdani-type Fuzzy Rule Based system which is used to quantify facial expression from facial
features. The comparison of proposed methodology with other state of the art methods is carried
out.
Aruna Chakraborty et al, 2009, introduced a method for emotion recognition from facial
expressions and its control using fuzzy logic, where the expressions of the human face would be
recognized by applying the fuzzy rule implementation. An effective application is invented by
B. K. Bose, 1994, about the expert systems, fuzzy logic, and neural network application in power
electronics and motion control. It was a multiple application system. Dae-Jin Kim and
Zeungnam Bien, 2003, developed an algorithm of Fuzzy Neural Networks (FNN) - based
approach for Personalized Facial Expression Recognition with Novel Feature Selection Method,
where this method was the combination of Artificial Neural Network and Fuzzy Inference
System. This method was used to recognize the different expressions of the human face. Dennis
Gillette and Ping Zhang did an effective survey on Human-Computer Interaction And
Management Information Systems: Applications. S. Dongcheng, J. Jieqing , 2010, introduced a
method of facial expression recognition based on DWT-PCA/LDA, the multiple application
approach. Esau N, et al, 2007, given an approach for Real-Time Facial Expression Recognition
using a Fuzzy Emotion Model, that effectively recognizes the emotions of the human face.
Francisco Herrera and Luis Magdalena, 1997, published a tutorial on genetic Fuzzy Systems
gives a detailed description on biological features of human faces using fuzzy logic. P. S.
Hiremath and Ajit Danti, 2005, developed a methodology on fuzzy-rule based method for human
face detection, that effectively detects the human faces by applying fuzzy inference system. A.
Jamshidnezhad, 2011, designed an approach that learns Fuzzy Model for Emotion Recognition
that recognizes the emotions of the human face. B. Jaychandra, simulated Speed Sensorless
Operation of Vector Controlled Induction Motor Using Neural Networks. Khanum A et al,
2009, contributed a research on Fuzzy case-based reasoning for facial expression recognition,
that efficiently recognizes the human facial expressions based on Fuzzy rule. G. Klir and B.
Yuan, 2010, published a study material on Fuzzy sets and Fuzzy Logic – Theory and
Applications, that gives a detailed description on the applications and usage of the fuzzy rule.
Kyoung- Man Lim et al, designed an algorithm for face recognition system using Fuzzy Logic
and Artificial neural network, that effectively recognizes the face in a given still image. S. Y.
Lee et al, 1996, introduced a method that recognizes the human front faces using knowledge
based feature extraction and neuro-fuzzy algorithm, a multiple application approach, that uses
geometrical facial features of the face along with Artificial neural networks plus fuzzy inference
systems. Maedeh Rasoulzadeh, 2012, did a research on facial expression recognition using
Fuzzy Inference System. Milki et al, 1991, designed an effective application for Vector control
of induction motor with fuzzy P-I controller. Muid Mufti and Assia Khanum, 1991, proposed an
approach on Fuzzy Rule-Based Facial Expression Recognition in a precise manner. Mufti M and
Khanam A, 2006, solved an unique problem on Fuzzy Rule Based Facial Expression
Recognition. M. Nasir Uddin, 2002, pursued a study on performances of Fuzzy-Logic-Based
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Indirect Vector Control for Induction Motor Drive, an effective approach on fuzzy logic.
Ralescu A and Hartani R, 1995, studied some issues in Fuzzy and Linguistic Modeling. D. H.
Rao and S. S. Saraf, 2007, conducted a detailed study of Defuzzification Methods of Fuzzy
Logic Controller for Speed Control of a DC Motor.
Several researchers contributed their work on emotions, emoticon recognition using
facial expressions using fuzzy inference system and neural networks etc. namely M.S.Ratliff and
E. Patterson, 2008; T. A. Runkler, 1996; M. Schmidt et al, 2010; N.Sebe et al, 2005; Starostenko
O et al, 2010; TakKuen John Koo, 1996; T. Takagi and M. Sugeno, 1985; L. H. Tsoukalas and
R. E. Uhrig, 1997; Ushida H et al, 1993; M. Usman Akram, et al, 2008; V. P. Vishwakarma et al,
2010; T. Xiang et al, 2008;
1.1 Fuzzy Inference System
Fuzzy inference is the process of formulating the mapping from a given input faces to an
output facial expressions using fuzzy logic and membership function. In this paper, Mamdani's
fuzzy inference method is used and it expects the output membership functions to be fuzzy sets.
After the aggregation process and defuzzification, crisp decision is made on facial expression as
shown in Figure-1.
1.2 Face Database
The proposed methodology is experimented on database of the faces of people of
different dimensions of happy expressions viz Normal, Bit smiley and Loud Laugh. There are
1000 facial images in this database, among them 700 images were used for training and
remaining 300 images were used for testing purpose. Each image is normalized to a size of 64 ×
64 dimensions for optimum computational cost. Sample facial expression images are shown in
figure-2.
Figure-1:Fuzzy Inference System for facial expressions.
Expressions
Output
Faces
I
N
P
U
T
Fuzzification DefuzzificationRule Evaluation
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The rest of this paper is organized as follows. The Proposed Methodology is explained in
Section 2. The Proposed Algorithm is described in section 3. In Section 4, Experimental results
are presented and Conclusions are drawn in section 5.
2. PROPOSED METHODOLOGY
This paper proposes an effective method for quantification of human facial expressions from
facial images. Block diagram of the proposed method is given in Figure-3.
Figure-3 :Block diagram of the proposed Methodology
The Proposed algorithm has been implemented to classify input images into one of three
happy expression using Mamdani-type Fuzzy Rule Based system In order to improve the
Figure-2: Samples of the facial images in the dataset
Input Face Image
Rule Evaluation
Fuzzification
Defuzzification
Expression
Recognition
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efficiency of the performance, mean vector is obtained for each image is used in the fuzzy
inference system as shown infigure-1.
2.1Quantification of Facial Expressions
It is assumed that every human face is having the same geometrical configuration.
Feature extraction method uses the mean of the image as input to the fuzzy system. This
improves the efficiency of the performance. Geometrical features of the face are fed to mamdani-
type fuzzy system for quantification using trapezoidal membership functions. This system is
capable of quantifying 3 basic forms of happy expressions that are No Happy, Bit Smiley, Loud
Laugh as shown in Figure-4 & 5.
The output of the fuzzy system is defuzzified to depict either the given face is in happy,
or bit happy or loud laugh. This can be represented by the equation(1).
≤ ≤ ≤ b≤ ≤ b≤ ≤ d ≤ d
Figure-5 : Representation of Quantification of Happy Expression
Figure-5: Representation of Trapezoidal Membership Function
No Happy No Happy
Loud Laugh
a b c d
Bit Happy Bit Happy
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, , , , =
0 ≤
−
−
≤ ≤
1 ≤ ≤ ℎ
−
−
≤ ≤
0 ≥
(1)
Where a,b,c,d values are determined empirically for quantification of expression.
The experimental results of quantification of happy expressions are shown in the figure-6.
3. PROPOSED ALGORITHM
Proposed algorithm for expression quantification from the given image is as given below:
Input: Query face
Output: Quantification of happy expression into No happy, Bit happy or Loud laugh
Step 1: To Train: Input all n face images to the Fuzzy Inference System.
Step 2:Define the Trapezoidal membership function for different expressions and compute
membership values for a,b,c,d as shown in Figure 5
Step 3: To Test: Compute membership value x for the query face using equation (1)
Step 4: Evaluate facial expression using equation (1).
4. EXPERIMENTAL RESULTS
In this research, there are 1000 gray-scale facial images used for experiment in which 300
images are used as training data and the remaining are used as test images. Each image size is
normalized to 64×64 dimensions. The proposed Algorithm have shown good robustness and
The person is not happyThe person is not happyThe person is not happyThe person is not happyThe person is not happy
The person is bit smileyThe person is bit smileyThe person is bit smileyThe person is bit smileyThe person is bit smiley
The person is laughing The person is laughing The person is laughing The person is laughing The person is laughing
Figure-6 : Sample Experimental Results of happy expression
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reasonable accuracy for the test set with low complexity and is suitable for real time facial
animation, image surveillance, mood analysis applications.
The success rate for quantification of happy expressions are 94.00%, 95.00% and 96.00%
for no happy, bit happy and loud laugh respectively. Therefore, the overall success rate for test
images is 95.00% as shown in the below graph. The average recognition time of each test image
is 0.30 seconds on a Pentium Quad Core processor with 2 GB RAM.
Figure-7 :Comparison of proposed method with other existing methods
As shown in the above graph, the proposed method has 95% of success rate when
compared to all others existing methods. The below table shows the details of comparison of the
proposed method and the other state of the art techniques and their respective success rates.
Sl.No Authors Method used Success
Rate(%)
1 Maedeh Rasoulzadeh [10] FIS 92.3
2 Akanksha Chaturvedi and AlpikaTripathi
[1]
Fuzzy Rule-based System 87.6
3 The Duy Bui et al [24] Fuzzy Rule Based System 80.8
4 Aleix Martinez and Shichuan Du [2] Emotion Model 94.1
5 Ashutosh Saxena, et al [5] Geometric model 90
6 Jyoti Mahajan and Rohini Mahajan [18] ANN 70
7 Prasad M and Ajit Danti [30] SUSAN Edge Operator 44
8 Jiequan Liet.al [17] Emotion recognition system 90
9 Anissa Bouzalmatet.al [3] Neural Network and
Fourier Gabor Filters
87
10 Hiroshi Kobayashi et.al [14] Neural Network 90
0
10
20
30
40
50
60
70
80
90
100
Comparison
Comparison
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11 Proposed Method
Dileep M R and Ajit Danti
FIS 95
Table-1 : Comparison with state of the art methods
However, proposed method fails to detect the side-view faces, occluded faces and partial
face images as shown in figure-8.This is due to missing facial features in the process of
recognition of facial expressions.
5. CONCLUSIONS AND DISCUSSIONS
In this paper, a fast and efficient quantification of expression system is proposed to
classify a facial image into different modes of happy expression groups using Mamdani-type
fuzzy system. Mamdani-type fuzzy rule based system recognizes three levels of same happy
expression namely No Happy, Bit Happy and Loud Laugh. The proposed method is better in
terms of speed and accuracy with success rate of 95% and performance is comparable to other
state of the art methods.
In future studies, misclassifications are reduced by further improvement in the proposed
system so that it becomes more pertinent to the design of a real-time video surveillance system.
ACKNOWLEDGEMENT
I would like to thank my guide Dr Ajit Danti, Director, Dept of Computer Applications,
J N N College of Engg, for helping me to carry out this analysis. I appreciate the helpful
comments and suggestions of Dr T Devi, Dept of Computer Applications, Bharathiar University
and all the anonymous reviewers.
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AUTHOR’S PROFILE
Mr. Dileep M R is currently working as Lecturer in the Dept. of Computer Science, Alva’s College, a unit of Alva’s
Education Foundation, Moodbidri, Karnataka, India. He has 4 years of experience in various capacities such as
Teaching, Administration and Research. Research interest includes Image Processing, Neural Networks, Fuzzy
Inference Systems, Database Applications, Software Engineering, Data Mining and so on. He has presented
number of research papers in National and International Conferences and Published number of research papers in
the reputed International Journals including SCI, SCOPUS indexed journals and IEEE Xplore digital library which
are freely available online. He has Completed Master of Computer Applications (MCA) from Visvesvaraya
Technological University, Belgaum, Karnataka, in the year 2013.
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Dr. Ajit Danti is currently working as Director and Professor in the Dept. of Computer Applications, Jawaharlal
Nehru National College of Engineering, Shimoga, Karnataka, India. He has 26 years of experience in various
capacities such as Teaching, Administration and Research. Research interests include Image Processing, Pattern
Recognition and Computer Vision. He has published more than 75 research papers in the International Journals and
Conferences. He has authored 3 books published by Advance Robotics International, Austria(AU) and Lambert
Academic Publishing, German which are freely available online. He has more than 250 citation index in the google
scholar and several papers are indexed in DBLP, SCI, Scopus, IEEE Explore etc. He has Completed Ph.D degree
from Gulbarga University in the year 2006. He has Completed Masters Degree in Computer Management from
Shivaji University, Maharashtra in the year 1991 and M.Tech from KSOU, Mysore in the year 2011 and Bachelor of
Engineering from Bangalore University in the year 1988.
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The Quantification of Human Facial Expression Using Fuzzy Logic

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