DOC - Robotics Lab

Human-Computer Interface Course – Final Project
Lecturer: Dr. 連震杰
Student ID: P78971252
Student Name: 傅夢璇
Mail address: [email protected]
Department: Information Engineering
Laboratory: ISMP Lab
Supervisor: Dr. 郭耀煌
Title: Real-time classification of evoked emotions using facial feature tracking and
physiological responses
Jeremy N. Bailenson, Emmanuel D. Pontikakis, Iris B. Mauss, James J. Gross, Maria E.
Jabon, Cendri A.C. Hutcherson, Clifford Nass, Oliver John. International Journal of
Human-Computer Studies. Volume 66 Issue 5, May, pp. 303-317, 2008.
Keywords: Facial tracking; Emotion; Computer vision
They propose real-time models to predict people emotion through subjects watching
videotapes and their physiological responses. The real emotion is extracted from the
facial expressions while watching videotapes instead posed. There are two emotion
types that are amusement and sadness to be exploited. The emotion intensity is also
observed. The experiment is split into three aspects which are performance
measurement, model choose comparison and various specific tests.
There are many applications and technologies of facial tacking are achieved by
camera and image technologies. In Japan, some cars are installed cameras in the
dashboard to insure the driving security. Besides, the cameras are also used to detect
the drivers’ emotion statuses which are angry, drowsy or drunk drivers. First goal of
facial emotion system development is to assist the human-computer interface
applications. The other goal is to understand people emotion from their facial
Related work
There are at least three main ways in which psychologists assess facial expressions of
emotions (Rosenberg and Ekman, 2000)
1) naı¨ve coders view images or videotapes, and then make holistic judgments
concerning the degree on target faces in the images. But the limitation is in that
the coders may miss subtle facial movements, and in that the coding may be
biased by idiosyncratic morphological features of various faces.
2) use componential coding schemes in which trained coders use a highly
regulated procedural technique to detect facial actions such as the Facial Action
Coding System (Ekman and Friesan, 1978). The advantage is this technique
including the richness of the dataset. The disadvantage is the frame-by-frame
coding of the points is extremely laborious.
To obtain more direct measures of muscle movement via facial
electromyography (EMG) with electrodes attached on the skin of the face. This
allows for sensitive measurement of features, the placement of the electrodes
is difficult and also relatively constraining for subjects who wear them. However,
his approach is also not helpful for coding archival footage.
Part 1- Actual facial emotion
The stimuli are used as the inputs which elicit intense emotions from people
watch videotapes. The actual emotional behavior is higher accessed than in
studies that used deliberately posed faces. Paul Ekman el. Propose the
Duchenne Smiles that is the automatic smiles involving crinkling of the eye
corners. The automatic facial expressions appear to be more informative about
underlying mental states than posed ones (Nass and Brave, 2005).
Part 2- Opposite emotions and intensity
The emotions were coded second-by-second by using a linear scale for
oppositely valenced emotions of the amusement and sadness. The learning
algorithms are trained by both binary set of data and linear set of data spanning
a full scale of emotional intensity.
Part 3- Three model types
Hundreds of video frames rated individually for amusement and sadness are
collected from each person enable to create three model types. The first type is
a ‘‘universal model’’ which predicts how amused any face is by using one set of
subjects’ faces as training data and another independent set of subjects’ faces
as testing data. The model would be useful for HCI applications in bank
automated teller machines, traffic light cameras, and public computers with
webcams. The second one is an ‘‘idiosyncratic model’’ predicts how amused or
sad by using training and testing data from the same subject for each model. It
is useful for HCI applications in the same person who uses the same interface.
For example, driving in an owned car, using the same computer with a webcam,
or any application with a camera in a private home are applications of the
idiosyncratic model. Thirdly, a gender-specific model that trained and tested
using only data from subjects in same gender is proposed. This model is useful
for HCI applications target a specific gender. For example, make-up
advertisements directed at female consumers, or home repair advertisements
targeted at males.
Part 4- Features
The data is not only extracted from facial expressions but also from subjects’
physiological responses including Cardiovascular activity, Electro dermal
responding and Somatic activity. The facial features from a camera and the
heart rate obtains from the hands gripping the steering wheel.
Part 5- Real-time algorithm
The real-time algorithm that is designed to predict emotion from computer
vision algorithms of facial features detecting and real-time physiological
measures extracted is proposed. There are applications on respond to a user’s
emotion such as cars seek to avoid accidents for drowsy drivers or
advertisements seek to match their content to the mood of a person walking.
The amusement and sadness are targeted in order to sample positive and negative
emotions. The 15 physiological measures were monitored. The selected films
induced dynamic changes in emotional states over the 9-min period which enables
varying levels of emotional intensity across participants.
Data collection
Training data: It was taken from 151 Stanford undergraduates watched movies
pretested to elicit amusement and sadness while they watch videotapes and
physiological responses were also assessed.
In the laboratory session, firstly, the participants should watch a 9-min film clip which
was composed of an amusing, a neutral, a sad, and another neutral segment (each
segment was approximately 2min long). From the larger dataset of 151, randomly
chose 41 to train and test the learning algorithms
 Expert ratings of emotions
It was anchored at 0 with neutral and 8 with strong laughter for amusement
and strong sadness expression. Average inter-rater reliabilities were satisfactory,
with Cronbach’s alphas= 0.89 (S.D.= 0.13) for amusement behavior and 0.79
(S.D.= 0.11) for sadness behavior.
Physiological measures
The 15 physiological measures were monitored including heart rate, systolic
blood pressure, diastolic blood pressure, mean arterial blood pressure,
pre-ejection period, skin conductance level, finger temperature, finger pulse
amplitude, finger pulse transit time, ear pulse transit time, ear pulse amplitude,
composite of peripheral sympathetic activation, composite cardiac activation,
and somatic activity shown as the figure.
System architecture
The videos of the 41 participants were analyzed at a resolution of 20 frames per
second. The level of amusement/sadness of every person for every second was
measured from 0 (less amused/sad) to 8 (more amused/sad). The goal is to predict
at every individual second the level of amusement or sadness for every person. The
emotion recognition system architecture from facial tracking output and
physiological responses is shown as the figure.
For measuring the facial expression of the person at every frame, the NEVEN Vision
Facial Feature Tracker is used. 22 points are tracked on a face with four blocks which
are mouth, nose, eyes and eyebrow.
Predicting emotion intensity
The WEKA software package is chosen as the statistical tool, the linear regression
function using the Akaike criterion for mode selection. Two-fold cross-validation was
performed on each dataset using two non-overlapping sets of subjects. The separate
tests are performed both sadness and amusement. Three test are using face video
alone, physiological features alone, and using both of them to predict the expert
The table below demonstrates the intensity of amusement is easier predicted than
that of sadness. The correlation coefficients of the sadness neural nets were
consistently 20–40% lower than those forthe amusement classifiers.
The table shows the facial features performed better than the classifiers only using
the physiological ones. The dataset is processed to discrete the expert ratings for
amusement and sadness. In the amusement datasets, all the expert ratings less than
or equal to 0.5 were set to neutral and 3 or higher were discretized to amused. On
the other hand, in the sadness datasets, all the expert ratings less than or equal to
0.5 were discretized to neutral and 1.5 or higher were discretized to sad.
Emotion classification
A Support Vector Machine classifier with a linear kernel and a Logitboost with a
decision stump weak classifier using 40 iterations (Freund and Schapire, 1996;
Friedman et al., 2000) is applied. Each dataset uses the WEKA machine learning
software package (Witten and Fank, 2005). The data is split into two non-overlapping
datasets and performed a two-fold cross-validation on all classifiers.
The precision, the recall and the F1 measure is defined as the harmonic mean
between the precision and the recall. For a multi-class classification problem with
classes Ai, i=1,..,M and each class Ai having a total of Ni instances in the dataset, if
the classifier predicts correctly Ci instances for Ai and predicts C’I i instances to be in
Ai where in fact those belong to other classes (misclassifies them), then the former
measures are defined as
The discrete classification results for all-subject datasets is shown as
Experimental results
The linear classification results for the individual subjects is shown as
The discrete classification results for the individual subjects is shown as
These two tables show that the performance of amusement is better than sadness.
Linear classification results for gender-specific datasets as table
Discrete classification results for gender-specific datasets as the table
These two tables indicate in the gender of male emotional responses than female.
A real-time system for emotion recognition is presented. A relatively large number of
subjects watched videos through facial and physiological to recognize the feel of
amused or sad responses. A second-by second ratings of the intensity with expressed
amusement and sadness to train coders. The results demonstrated better fits in the
performance of emotion categories than intensity, the amusement ratings than
sadness, a full model using both physiological measures and facial tracking than use
alone and person-specific models than others.
• The emotion recognition through facial expressions while watching
videotapes is not strong prove become of the limitations in content of
Otherwise, the correctness of the devices of physiological features collection
are also considered
The statistics in the aspects of emotion intensity is not significant improve
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