Facial Expression Based Real-Time Emotion Recognition Mechanism for Students
with High-Functioning Autism
Hui-Chuan Chu1, William Wei-Jen Tsai3, Min-Ju Liao2, Wei-Kai Cheng3, Yuh-Min Chen3, Su-Chen Wang3
1
Department of Special Education, National University of Tainan, Taiwan
2
Department of Psychology, National Chung-Cheng University, Taiwan
3
Institute of Manufacturing Information and Systems, National Chen Kung University
ABSTRACT
The emotional problems of students with autism may greatly affect their learning in e-learning environments. This paper presents
the development of an emotion recognition mechanism based on a proposed emotional adjustment model for students with
high-functioning autism in a mathematics e-learning environment. The physiological signals and facial expressions were obtained
through evoking autistic students’ emotions in a mathematical e-learning environment, and used for training the emotion
classification model and to verify the performance of the emotion classification mechanism. In total, 34 facial features were
obtained experimentally that were conducted by using a counterbalanced design, and 46% of features were further extracted by the
chi-square method, Information Gain (IG), and Wrapper feature selection methods. A Support Vector Machine was used to train
the emotion recognition model and assess the performance of the proposed emotion recognition mechanism. Four emotional
categories, calmness, happiness, anxiety, and anger, were identified based on the 34 features. The accuracy rate of the recognition
model was 82.64%. By balancing feature reduction and recognition accuracy, using Wrapper and SVM, we were able to reach an
81.63% accuracy rate and reduce feature size by 46%. This Emotion Recognition mechanism can operate with an affective tutoring
system by recognizing emotional changes in students with high-functioning autism, thus enabling timely emotional adjustments.
KEYWORDS
Students with high-functioning autism, Face measures, Emotion recognition
INTRODUCTION
Improving achievement for all students, including students with disabilities, is a focus of current educational reform efforts in
many countries. However, because students with disabilities learn differently from normally achieving students, a major issue in
special education is the provision of adaptive education in various academic disciplines, and developing learning approaches for
students with disabilities based on their diverse characteristics.
Research has indicated that elementary and secondary school students consider mathematics to be the most difficult subject
(Mazzocco & Myers, 2003). As students advance through the grades, their difficulties with mathematics continue to increase,
and the ratio of those who hate math increases, while learning interest and motivation plummet, which all work to reduce
learning effectiveness. Students with disabilities may have more severe problems in this regard, indicating that it is important for
researchers to explore innovative instructional methods and delivery systems for more effectively addressing the serious
challenges this population faces (Fuchs & Vaughn, 2012).
Many e-learning methodologies have recently been proposed. Advances in information and communication technologies have
had some positive effects, such as eliminating time and space barriers, reducing learning costs, and providing adaptive learning
services, and are significantly improving learning effectiveness. Furthermore, among the various disabilities students may have,
those with high-functioning autism—despite their deficiencies in social communication skills and resistance to change (Wainer
& Ingersoll, 2011)—often have excellent spatial cognition and memory. For these students, e-learning environments can meet
their behavioral and cognitive requirements (Swezey, 2003; Cheng & Ye, 2010), stimulate their learning motivation, and resolve
many difficulties they experience in the learning process. (Vullamparthi et al., 2011).
A profound emotional impairment is a core feature of autism spectrum disorder (ASD) (Begeer et al, 2008; Fabio et al, 2011),
frequently influencing internal and external emotional factors in the learning process, which may arise during failures and
setbacks. Emotional reactions from students with ASD are typically very intense, and can cause anxiety or other negative
attitudes (Reaven, 2009). Despite the numerous benefits that e-learning environments can provide for students with ASD,
emotional problems are a main cause of interruptions in learning, reducing learning effectiveness. The application of the field of
special education e-learning typically emphasizes cognitive assistance, instructional design, and multimedia presentation for
teaching materials, but often neglects the emotional experiences of students with ASD during learning. Therefore, providing
emotion-related assistance to students with ASD is an emerging issue.
Since the concept of affective computing was first proposed by Picard (1997), e-learning platforms have evolved from intelligent
tutoring systems (ITSs) (Schiaffino et al., 2008; Curilem et al., 2006) to affective tutoring systems (ATSs) (Mao & Li, 2010;
Christos & Anastasios, 2009; Shen et al., 2009; Afzal & Robinson, 2011) that integrate new technologies with the conventional
e-learning environment. The ATSs are based on the idea that an emotion detection mechanism can be developed that enables a
learning system to automatically identify a student’s emotional state. Fortunately, this feature satisfied the requirement of
emotional adaptation for students with ASD. Currently, the main research direction is the identification of emotional features,
such as physiological signals, facial expressions, speech, and physical postures (Moridis & Economides, 2008).
Although using an ATS for autistic students can increase their learning performance, some critical issues remain. Students with
ASD have severe difficulties with communication-related emotions, interacting with society, and presenting their own facial
expressions and describing those of others. Notably, their facial expressions change less than those of ordinary people (Bieberich
& Morgan, 2004; Czapinski & Bryson, 2003). Physiological sensors are unsuitable for students because wearing a device makes
them uncomfortable, and these negative feelings may cause students to be restive in the learning process. Additionally, how to
improve the real-time recognition performance is an important issue for timely emotional adjustment.
To address the challenges described above, this work utilizes the facial-expression-based approach to recognize student
emotional states in the learning process for two reasons. First, human emotional expressions come in numerous forms, such as
facial expressions, body language, and vocalizations. Among these, 55% of emotional information is conveyed by facial features
(Mehrabian, 1968). Second, facial-feature-based recognition does not use body-based devices. Addressing performance issues
for real-time emotion recognition using the feature selection technique to reduce the dimensions of data a set is necessary.
Furthermore, accelerating training time, preventing over-fitting issues, and identifying which features are useful in recognition
process are of critical importance.
PROPOSED AFFECTIVE TUTORING SYSTEM FRAMEWORK
An ATS framework was designed that works by identifying the students’ emotional states combined with emotional adaptation
in the learning process (Fig. 1). The model has two layers, a cognitive layer and an emotional layer, which support students in
different ways.
Affective Tutoring System Framework
Cognitive Content Repository
Cognitive Adaptation
Mid-term
Assessment
Domain Knowlegde
Model
Learning Adaptation
Model
Cognitive
Adaptation
Students
Model
Cognitive Layer
Remote
Supporting
Parent
Parent // Teacher
Teacher
Advisor
Advisor // Expert
Expert
Learning Plan
Execution
Adaptive Learning
Outcomes
Assessment
Student
Student with
with ASD
ASD
Affective Content Repository
Affective Layer
Affective Adaptation
Emotion
Recognition
Emotion
Adaptation
Emotion Model
Emotion
Adaptation Model
Figure 1. Affective Tutoring System for students with autism
Analysis of
Strategy
Effectiveness
EMOTION RECOGNITION MECHANISM
Online Recognition Phase
Emotion
Emotion
Recognition
Recognition Result
Result
PC With WebCam
Emotion
EmotionFeatures
Features
recognition
Modeling Phase
Feature
Extraction
Video Source
Recognition Model
Construction
generate
Emotion
Model
Emotion
EmotionTagging
Tagging
Emotion
EmotionFeatures
Features
Evaluation, Optimization
Figure 2. Emotion Recognition Mechanism
An emotion classification mechanism for students with high-functioning autism was designed to resolve emotional recognition
problems and ensure smooth operation of the affective system framework. The Emotion Recognition Mechanism has a modeling
phase and an online recognition phase. In the modeling phase, the proposed mechanism extracts a feature set from video records
of an emotion evocation experiment, and builds the emotion recognition model. The online recognition phase extracts a feature
set from the webcam as input to the emotion model for real-time emotion recognition. These are explained in more detail below.
Feature Extraction
The goal of the facial expression feature extraction process (Fig. 3) is to identify changes in the facial expressions of students
with ASD. Facial feature anchor points were transformed into expression features characteristic of different emotional states,
producing 34 primary facial features. This process included facial feature tracking, expression feature extraction, and expression
feature normalization.
Facial Feature
Tracking
Facial Feature
Point
Facial Feature
Preprocessing
Video
Frameset
Distance-based
Feature Set
Angle-based
Feature Set
Statistical Processing
Normalization
Synthetic Feature
Set
Figure 3. Facial Expression Feature Extraction Process
Facial Feature Tracking: Changes in facial expression are closely related to facial features. Thus, the number and position of the
corresponding points of facial features must be defined for subsequent facial feature positioning and tracking. Most studies used
two-dimensional coordinate positioning on the X-axis and Y-axis. The core technology developed in this work, using FaceAPI
(Zintel, 2008), which considers the Z-axis, in the Cartesian head three-dimensional coordinate positioning method to
automatically follow features in sequences of human face images and capture feature point coordinates. This allows the heads of
subjects to turn +/- 90° while preserving excellent tracking. Figure 4 shows the selection of facial feature points, with a total of
21 points. Of these, five points are for each eye, three points are for each eyebrow, four points are for the mouth, and one point
on nose.
LbP1
RbP2
LbP2
LbP3
RbP3
RbP1
LD15 LA10
LeP2
LeP1
LeP4
ReP4
LeP3
ReP2
ReP1
ReP3
LD6
D1
LD8
RA11 RD16
RD7
RD9
ReP5
LeP5
nP
RD4
LD3
A14
mP2
mP3
mP1
y
x
LA12
y
x
mP4
RA17
D5
A13
D2
z
z
Figure 4. Facial feature point distribution
Figure 5. Facial feature distances and angles
Figure 6. An Example of Signal Series (Feature D5)
Facial Feature Preprocessing: In total, 21 feature points were acquired. Feature distances and angles for judging facial
expressions were defined between each point to serve as features of a facial expression (Fig. 5). Twelve feature distances and
five feature angles were calculated using the coordinates of facial feature points, as in Eqs. (3) and (4):
- (3)
- (4)
Where
D2: mouth width.
mP1: right angle positioning point of the mouth.
mP3: left angle positioning point of the mouth.
Statistical Processing: The average and standard deviation of each feature change is calculated for preset time intervals (Fig. 6).
The time interval value depends on configurations of the data collection procedure (see “dataset” section).
Normalization: As the range of raw data values varies widely, SVM algorithms will not work properly without normalization. If
one feature has a broad range of values, the distance will be governed by this particular feature. Therefore, the range of all
features must be normalized, such that each feature contributes a rational ratio to the final distance:
- (5)
Recognition Model Construction
This section introduces how to construct an emotion recognition model using a machine learning approach. This process includes
feature selection, and model training. These are explained in sequence below:
Feature Selection
Filter
Synthetic
Feature Set
SVM Model
Training
Emotion
Model
Model
Evaluation
Wrapper
Figure 7. Construction Process for the emotion classification model
Feature selection
Feature selection algorithms can be classified as filters and wrappers. With filter algorithms, features are first scored and ranked
according to relevance to the class label, and are then selected according to a threshold. Wrappers utilize a specific machine
learning algorithm as a black box to score a feature subset by evaluating its predictive power. The filter approach is often
computationally cost-effective, but wrapper approach usually leads to a better generalization in data separation. This work used
both approaches to search for the optimized feature set and compare it. In filter-based feature selection, this work applied IG
(Quinlan, 1979) and chi-square value to rank each attribute.
Chi-Square Value ( ): This method is based on the measurement of the lack of independence between an emotion feature and
emotion classes, and can be compared to the chi-square distribution with one degree of freedom to assess extremeness.
Information Gain (IG): According to information theory, IG is a feature selection method that is fundamentally defined as “the
amount of information prior to testing” subtracted by “the amount of information after testing”. As in Eq. (5), the amount of
information a feature contains was calculated to judge whether the feature needed to be selected. This method was used to reduce
the original feature sets to feature subsets that were easier to process, thereby reducing the number of feature dimensions. Thus,
IG was used in this work to calculate the amount of information in each emotion feature. “The amount of information before
testing” corresponds to this part of the study, and represents the calculation of the total amount of information contained in the
emotional categories. “The amount of information after testing” is the total amount of information in certain single emotion
features after information S was categorized. As the IG increases, the amount of information in an emotional feature increases,
which is important to the classification algorithm.
InfoGain( Aj ) = Info(S ) - Entropy( Aj )
Where Info(S ) : Total amount of information contained by all the emotion categories.
(8)
Entropy( Aj ) : Total amount of information contained in a certain emotion feature after information is categorized.
A j : A certain single emotion feature.
For wrapper feature selection, this work used an SVM as a black box and iterated a feature set by Sequential Forward Selection
(SFS) to search for the best feature set.
Emotion recognition model training
Support Vector Machine (SVM): An SVM is a machine learning method derived from statistical learning theory. It uses
structural risk minimization (SRM) for rules, constructs a separating hyperplane through the learning mechanism, and
differentiates data from two or more classes. This work posits a multi-class emotion classification problem, which is an
inseparable linear type. The radial basis function (RBF) kernel (Hsu et al., 2003) must be used to calculate an equation such as
Eq. (9), to serve as the kernel function. The feature data are transformed from the input space to the feature space, and linear
classification is then applied to the space.
K (xi , x j ) = exp(- g || xi - y j ||2 ), g > 0
(9)
The RBF kernel is useful for classifying nonlinear, highly dimensional data, as it only requires an adjustment of cost (C) and
gamma (γ). Therefore, finding the optimized C and γ is critical. The SFS was used to seek the optimal combination of parameters
(C and γ) to train the enhanced SVM-based emotion classification model.
EVALUATION RESULT
Dataset
Our previous study (Chu et al., 2012) proposed a dual-mode offline classification mechanism for high-functioning autism
students; this work furthers that research. To improve real-time facial feature recognition, this work removed physiological data,
used legacy video source files for data refinement, and captured video data with 15 frame per second (FPS) at 30-second
intervals (450 frames). If data loss or noise exceeded 20% during one interval (less than 360 frames were available) then it was
removed. In total, 592 emotion samples remained after filtering to construct the emotion recognition model. Among these
samples, 241 samples were tagged as calmness, 91 samples as happy, 218 as anxious, and 42 samples as angry. Figure 8 and 9
show the emotion evocation experimental environment.
Monitor and Data Collection
Webcam
Participant with Autism
Monitor
Parents
Monitor
Researcher
Special Education Teacher
Figure 8. Emotion evocation experimental environment
Figure 9. A student with high-functioning autism doing the
exercise
Evaluation Protocol
This work’s protocol was to evaluate model effectiveness and the proposed methodology (Fig. 10). In this work, 10-fold
cross-validation was applied to prevent over-fitting (Delen et al., 2005). The evaluation protocol is described below.
SMOTE
All Features
Non-SMOTE
(Raw Source)
Feature
Extraction
Feature Set
SVM Classifier
Wrapper
Evaluation
IG
Filter
Chi-Square
Feature Extraction
Emotion Recognition
Figure 10. The evaluation procedure
Number of features preserved: To optimize performance and identify the importance of features, this work determined the
number of features preserved.
TP Rate and F-Measure: After completing model training, precision (P), recall (R), and the F-measure were used to assess
performance in emotion classification using the following equation:
- (10)
- (11)
- (12)
Where:
TP: Number of emotion categories classified correctly.
FP: Number of emotion categories classified incorrectly.
FN: Number of samples belonging to certain emotion categories but classified incorrectly.
ROC (Receiver Operating Characteristic) AUC (Area Under Curve): The ROC curve is a standard technique for summarizing
classifier performance over a range of trades using true positive and false positive error rates (Swets, 1988). The ROC convex
hull can also be used as a robust method of identifying potentially optimal classifiers (Provost & Fawcett, 2001).
Result of Feature Selection
Through statistical processing, normalization,34 face-related emotional features were obtained as input for feature selection.
According to analytical results, this work searched for the best configuration of feature selection through emotion recognition
evaluation. The feature selection details are listed below.
Table 1. Number of features preserved
Feature
Feature
Reduced Rate
Selection
Quantity
IG
15
65%
Chi-Square
15
65%
Wrapper
18
47%
Evaluation of Emotion Recognition Mechanism
In the evaluation protocol, all configuration sets were input variables for evaluation of the emotion recognition model. Testing
and verification used 10-fold cross-validation to obtain the final average correct rate and prevent over-fitting. The optimal
emotional classification accuracy was 68.24% (Table 4).
All
Features
IG
ChiSquare
Wrapper
Index
TP Rate (%)
F-Measure
ROC AUC
TP Rate (%)
F-Measure
ROC AUC
TP Rate (%)
F-Measure
ROC AUC
TP Rate (%)
F-Measure
ROC AUC
Table4. Result of Emotion Recognition Model
Calmness
Anxiety
Happy
74.30
71.10
59.30
0.710
0.706
0.632
0.752
0.767
0.771
71.00
68.30
47.30
0.668
0.665
0.528
0.712
0.733
0.707
73.40
73.90
47.30
0.704
0.709
0.528
0.746
0.769
0.707
75.10
71.10
47.30
0.702
0.703
0.541
0.742
0.765
0.711
Angry
38.10
0.457
0.680
28.60
0.393
0.636
31.00
0.406
0.647
33.00
0.412
0.656
Overall
68.24
0.679
0.755
63.34
0.626
0.714
66.55
0.658
0.742
66.39
0.657
0.739
CONCLUSIONS AND FUTURE WORK
Providing emotion-related assistance to students with ASD is important. This work applied a novel ATS framework to assist
high-functioning autistic students in e-learning, and an automated real-time emotion recognition mechanism that supports the
proposed ATS framework. The mechanism uses facial expressions to recognize emotions. The emotions of students with ASD
during the learning process, such as calmness, happiness, anxiety, and anger, were evoked by real mathematical e-learning
situations. 34 features were selected to build an emotion recognition model with the SVM. Emotion classification accuracy was
68.24%. Through cross-validation of all configuration subsets, including three feature selection methods, our work demonstrates
that using Chi-Square is the best solution for recognition because the rational trade off in accuracy lost is 1.69%, over half of the
feature set decreased (65%), and accuracy in recognizing negative emotions increases. This demonstrates that the mechanism is
feasible, effective, and practical. By combining it with an e-learning system, this approach could serve as effective and on-time,
unobtrusive monitor that assists students in learning for achieving a harmonious experience. On the other hand, the proposed
mechanism is a necessary component in an ATS for any purpose. However, the following limitations and issues deserve
comment.
First, children with autism spectrum disorders often have emotions that are highly correlated with some repetitive actions or
spontaneous non-verbal sounds. Therefore, these actions and sounds can also be combined to develop an emotion recognition
mechanism, and this may make the identification of emotions more accurate and faster. At the same time, a larger scale
experiment needs to be performed to ensure the stability of the emotion classification model.
Second, researchers developing an emotional adjustment model for use in mathematics e-learning not only need to consider the
accuracy of emotion recognition, but also develop emotional adaptation strategies for autistic students. Such strategies should
consider two important factors, emotions and learning, and then develop new types of teaching methods. Currently, few studies
are examining emotional adaptation strategies for students with disabilities in a mathematics e-learning context.
Finally, the development of emotion recognition and adaptation strategies are needed to improve mathematics e-learning for
autistic students. Since the ultimate goal of such work is to enhance learning effectiveness, the practical application of an
emotional adjustment model for learning will need to be undertaken and compared to traditional classroom learning or other
e-learning systems.
ACKNOWLEDGEMENT
The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research
under Contract No. NSC 100-2628-S-024-003-MY3.
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