Personalized Emotion Model Based on Support Vector Machine
Jin-bin Wu1, Wan-sen Wang1
1
Information Engineering Institute Capital Normal University, Beijing 100048, China
wujiabin88@163.com
Abstract - Emotion deficit is an intelligent in e-learning
technology research. The main purpose of the paper is based
on SVM (Support Vector Machine) through the samples
data analysis of the face area, interpupillary distance, eye
spacing and mouth curvature to build to the aversion degree,
cheer degree and pleasure degree based emotion model of
personality academic emotions. All of these lay the
foundation for emotional teaching in E-Learning System.
Keywords - academic emotions, emotion deficit,
E-Learning, Support Vector Machine
Ⅰ.INTRODUCTION
Analysis of the existing E-Learning system, we can
easily find a common phenomenon: the current system is
often web-based information technology “boilerplate” are
text-based teaching can be seen, usually posted on the
Internet, teaching practice, and related methods, this
teaching method is indifferent learning lack of
personalized teaching guidance, we generally call
“emotional deficit”[1]. The importance of emotions in the
E-Learning, University of Adelaide Professor Kerry
O'Regan, told that students conducted a survey of
distance learning, she found that the emotion was the key
to learning networks and was the essential factor in the
teaching and learning process[2]. In addition, according to
the psychological studies, emotional factors have an
important impact on the learning behaviors[3].
The main purpose of these researches is the
introduction of emotional teaching function in the
traditional E-Learning. With the rapid development of
information technology in the field of education,
e-learning has rapidly changed distance education. But
after the initial practice boom retreated, people gradually
return to rationality. E-Learning has many advantages, but
due to the lack teachers to participate and can not
completely replace the classroom educational activities of
teachers and students learning is not as conceived so
perfect. This emotional missing affected the teaching
effectiveness and widely used of distance education.
Meanwhile, Chinese educational psychologist Professor
Qing-lin Wu pointed out that intelligent e-learning system
is not only a truly personalized teaching system but also
is emotional [4].
II. BASIC CONCEPTION
This research focuses on learners' emotions and
emotional modeling. In general, the mood in psychology
with a number of different classification methods, the
each different method can be divided into several
different types.
Emotions
have
physiological
and
explicit
characteristics: the physiological characteristics mainly
refer to the obvious physiological changes inside the body,
such as breathing, heartbeat, etc. The explicit features
refer to the physical external changes, such as smiling,
frowning, etc. The explicit characteristics often refer to
the expression. Different emotional characteristics access
to information is different. Physiological characteristics
of access to information usually require a physical means
of galvanic skin contact. While expression information
can be obtain by video, audio and other non-contact
means. Visible, in network learning, expression method
access to information is practical and significant.
The expression can be divided into three types, such
as facial expressions, body expressions and language
expressions. Because the expression is the external
expression of emotions, so if a different expression has
recognized, it can be judged by different expression from
different emotions of the people, and also by the different
expressions of the people to express different emotions.
In the three expressions, the facial expression is the most
identification of emotional signs, the most sophisticated
to identify the specific mode of the different nature of
emotions. Emotional modeling is the core of this paper
and important aspects of emotional information in
E-Learning. In this paper, the extraction of facial
expression characteristics, its steps, the first analyses the
sample data for stability and reliability, and then
introduce aversion degree, cheer degree and pleasure
degree to describe the emotional state of the learners.
Select a few learners to information collection and
analysis the image filter as a sample. Experiment in a
variety of environments, the rapid classification of the
characteristics of small samples by SVM(Support Vector
Machine), build the aversion degree, cheer degree and
pleasure degree of sample data for modeling and analysis.
OCC emotion model is proposed by a book named
emotional cognitive structure, write by Ortony A, Clore G,
Collins A, which is also the earliest and most complete
one of the model study of human emotions. According to
the emotional causes, OCC emotion model is divided into
the following three categories: the results of the event, the
action of the agent, the object perception. The model
defines a total of 22 species basic emotions and the
relationship between their levels. The OCC model is not a
basic emotion set or a clear multi-dimensional space to
express their feelings to express emotion, but with
consistency cognitive export conditions. In particular, in
the model assumes that the emotional satisfaction and
dissatisfaction Agent, happy and unhappy event, and the
likes and dislikes object, they constitute the reaction of
the situation in a positive or negative tendency. The
model summarizes the standards, which includes 22 kinds
of emotional type used to generate the basic structure of
the rules, and these emotional types are derived by
different cognitive conditions.
In this paper, academic emotions, defined as three
dimensions[5], discuss the reverse of each other in the
most common of the six academic emotions: interested,
bored, excited, tired, happy and distress. In the basic
emotional space, A represents the interest, B is boredom,
C is excited mood, D is fatigue, E is a happy and F is
distressed. The origin of coordinates is removed from the
emotional space, because the origin of coordinates is
emotional state, so it does not meet the normal human
emotions.
(nu-support vector regression) and other problems,
including based on one-on-one algorithm to solve the
related many types of algorithm's problems. Support
vector machine is used to solve the problem of pattern
recognition or regression, the international scientific
community has not yet formed a unified view the
parameters choice and the choice of kernel function. This
also means that the parameter selection of the optimal
SVM algorithm can only use the excellent previous
experience, or comparative experiments, or large-scale
search, or use the package cross-validation function. Also
use other algorithms to achieve optimization, the
algorithms such as genetic algorithm[8], particle swarm
optimization (PSO)[9] and cats swarm optimization
(CSO)[10]. In this paper, due to the complexity of the
experimental constraints, only to choose the experts and
experience the results of selected parameters.
IV. COMPREHENSIVE EXPERIMENT
III. SUPPORT VECTOR MACHINE INTRODUCTION
[6]
Support vector machine
designed to solve
nonlinear problems of the small sample study and
classification. On the one hand, to overcome, the least
squares method is too simple, can not distinguish between
complex nonlinear classification designs; on the other
hand, support vector machine has good classification
ability, but neural network had the problem of overfitting
and underfitting. SVM technology, the most critical is the
selection of the kernel function: different kernel functions
have a great impact on the classification results. There are
several different ideas in the selected kernel function to
solve practical problems: The first use of an expert
transcendental knowledge for kernel function selected;
the other is the Cross-Validation method, kernel function
selection process, experimenting with different kernel
functions and parameters; the third using mixed kernel
function method (Smits et al. Proposed), which is using
different kernel functions combined to obtain better
performance, it is also the basic idea of the mixed kernel
function. On the whole, the parameter selection problem,
in essence, is an optimization problem.
In this paper, using the main advantage of the SVM
algorithm is to classify training data characteristics of the
facial expression modeling and has obtained good
experimental results. In the research, using libsvm[7]
toolbox in Matlab, it is developed by National Taiwan
University Professor LinChin-jen. The aim is to design a
simple, easy to use support vector machine (SVM) to
solve pattern recognition and linear regression package.
The software not only provides a compiled version of
Microsoft Windows, but also other operating systems.
The executable file is open source code, facilitate others
to improve and modify; the software can solve the C-SVC
(C-support vector), nu-the SVC(nu-support vector
classification), one-class SVM (distribution estimation),
epsilon-SVR (epsilon-support vector regression), nu-SVR
Due to the complexity of the human face, related to
the study about this experiment, I proposed the three main
concepts are aversion degree, cheer degree and pleasure
degree. Aversion is based on the face area and
interpupillary distance to locate positioning method.
Positioning and calculation of the face and the pupil of
the eye is to determine the learners in the learning process,
interested in learning the current content. Under normal
circumstances, when the detected face area and
interpupillary distance is larger, which means learners
leaned forward in the learning process, learning content is
relatively interested in, aversion for bigger; On the
contrary, when the changes in the hours means that
learners lean back, and not interested in learning content,
and even boredom, aversion for smaller.
Similarly, cheer degree of detect eye spacing
heterozygosity is to describe and judge the cheer extend.
And Pleasure degree, through the mouth upturned angle
to detect degree of pleasure in the learning process.
Verify the stability of the data to prove its stability. A
learner and B learner within two hours (every 60s tested
once) detected in the normal state of learning data.
Because of space constraints, I only cite the face area and
interpupillary distance of data analysis figure, similar to
other situations.
Detected sample data (the face area and interpupillary
distance) really focused within a certain range.
Accordingly, we propose a hypothesis: the face area and
ongoing testing to get a sufficient amount of data, we
believe that it is possible to meet the normal distribution,
if that were true, then we can change the scope of the
already mentioned, and then detect whether the current
learners in the normal learning state. Fig. 1 shows the
results of tests of the data sample, to demonstrate that
they meet the assumption.
Fig. 1.
Face area of a learner the normal reference curve
Fig. 3.
A. Input Variable Selection
Aversion degree of face area and interpupillary
distance, and the statistical analysis of previous data have
been found that these two sets of data for normal
distribution, indicating that learner’s mood is relatively
stable over a period of time.
We put this the face area and interpupillary distance
of 120 sets of data in two different learners classification
preprocessing, select 100 as the training set, the
remaining 20 as test set. Consider a simplified
classification of emotions into the four categories, which
are very interested, interested, tired and very tired. So one
On behalf of very tired, two is tired, three is interested
and four is very interest, enter is test data of human face
area and interpupillary distance. Fig. 2 shows the
relationship between category labels and face area,
interpupillary distance, asterisk is the distribution of
sample points.
Aversion degree of classification results
Operating results: Accuracy = 70% (14/20)
(classification).
The final classification results are as follows:
The experimental results can be seen from Fig. 3, the
blue represents the classification of the actual test set, and
red represents the prediction set classification, the
classification accuracy is 70%, the basic realization of the
successful implementation of the aversion degree
modeling and analysis. Successful implement of the
mapping from the face area and interpupillary distance to
four different emotions.
Other, cheer degree and pleasure degree have the
similar results. Accuracy can reach 95%, to obtain good
experimental results.
V .CONCLUSION
This paper is using fast learning classification
adopting support vector machine network of small sample
nonlinear characteristics based on the OCC emotion
model, aversion degree, cheer degree and pleasure degree
to establish academic emotions model in E-Learning. The
model provides the necessary basis of academic emotions,
is also a useful attempt of the SVM algorithm in the field
of emotion recognition, achieved the good results.
ACKNOWLEDGMENT
Fig. 2.
Aversion degree labels and property distribution
The research is supported by the National Natural
Science Foundation of China (Grant No.60970052) and
Beijing Natural Science Foundation (The Study of
Personalized e-learning Community Education based on
Emotional Psychology).
B. Data preprocessing
Training and test set were normalized preprocessing.
C. Training and prediction
Kernel function to select the radial basis kernel
function, the function C is selected as 1000.
D. Analysis of experimental results
REFERENCES
[1]Zhang Xiu-yu. Framework of an E-Learning system based on
affective computing. Journal of Hunan Institute of Science
and Technology (Natural Sciences) 2009, 22(4):51-54
[2]Kerry O’Regan. Emotion and E-Learning. JALN.2003,
7(3):78-92.
[3]Su Xiao-ping, Xu Yun-xi. Intelligent E-learning system
having emotion interaction function. Computer Engineering
and Design. 2009, 30(15), 3690-3693
[4]Wu Qin-lin. Educational Psychology ---- The book
dedicated to the teachers. Shanghai: East China Normal
University Press 2003
[5]Wang Zhi-liang. Artificial Emotion. Beijing: Mechanical
Industry Press, 2009
[6]Cores C,Vapnik. Support vector networks. Machine Learning,
1995, 20: 273-297
[7][EB/OL]http://www.csie.ntu.edu.tw/~cjlin/libsvm/in
dex.html
[8]Kang Haigui Li Mingwei Zhou Pengfei Zhao
Zehui.Prediction of traffic flow using support vector machine
optimized by chaos higher efficient genetic algorithm. Journal
of Wuhan University of Technology (Transportation Science
& Engineering), 2011, 35 (4):649-653
[9]Chen Wei,Mei Yi. Research on forecasting methods for
reduction ratio of pore closure in forging stock based on PSO
and SVM. Computer Engineering and Applications, 2011, 47
(27):243-245
[10]Wan-sen Wang, Jia-bin Wu. Emotion recognition based on
CSO&SVM in e-learning, ICNC, 2011