How to Detect a Loss of Attention in a Tutoring System
using Facial Expressions and Gaze Direction
Mark ter Maat
ABSTRACT
Tutoring systems try to help students with certain topics, but
unlike a normal teacher it is not very easy to notice when a
student is distracted. It is possible to detect the gaze direction to
detect a loss of attention, but this might not be enough. This
paper describes all the parts needed for a detection system that
uses the detection of facial expressions and gaze direction to
recognize if a student is distracted, plus several actions that can
be performed to get the student back.
Keywords
Loss of attention, distraction, facial expression recognition,
gabor wavelets, gaze direction.
1. INTRODUCTION
As computers evolve, the number of interaction possibilities
steadily grows. In early times, only a keyboard could be used to
tell the computer what you wanted and the computer could only
provide information back through text. Nowadays we still use
the keyboard, but we can also use other types of interaction to
communicate with a computer, e.g. with a mouse, with speech,
a touch screen, etc. And the computer also has new ways of
interacting with the user, e.g. with pictures, sound and movies.
These new ways of interaction also open new types of
communication, using different modalities. For example, the
computer can create a virtual teacher that tries to teach a child
how to write letters on a touchpad. Or the computer and the user
can meet each other in some kind of dialogue where the user
can speak to the computer, and the computer understands this
and talks back. In these examples it is a task of the computer is
to keep the user focused on the computer. This can be done by
giving interesting information, but a better approach is to detect
if the focus of attention of the user is on something else.
expressions (why these?) and Chapter 5 will explain how gaze
direction detection works. In Chapter 6 different collaborative
states are treated, and in Chapter 7 it will be explained how a
system can deal with the information gathered in these three
sub-systems. Chapter 7 will give some example situations.
2. BACKGROUND
According to the dictionary, attention is “Concentration of the
mental powers upon an object; a close or careful observing or
listening”. In other words, with attention a person is focusing
himself on another person or object. Focusing on something is
done by our senses: seeing, hearing, tasting and smelling. But
that are just the outer forms of perception and attention. People
use inner perception like the perception of feelings, emotions
and thinking as well [Akk06].
But the amount of input a person receives is overwhelmingly
large. That is why attention also means filtering out the
important information and leaving the other (less important)
information unprocessed [PLN02].
A computer system that has to detect the loss of attention can
not measure a lot of things; it can not measure exactly what the
user is thinking of, what he is feeling, or what he is listening to.
However, it is possible to pick up certain cues. For example, if
the user is watching it something else, he will probably turn his
head, and when he is listening to something else, he will
probably do the same. Also, when the user is thinking of
something else, he will make different facial expressions than
expected.
The orientation of the head can be determined by taking the
locations of the eyes and the tip of the nose. With simple
geometry the (rough) orientation of the head can be calculated
[UKM04]. The detection of facial expressions is a little bit more
difficult.
This article will mainly be on tutoring systems. In these manmachine systems a computer will try to teach a person
something. For example, the computer can give loads of
information, or it can give the person assignments to make.
During these teaching sessions, the person has to keep its
attention on the computer and the assignments. This means that
if the computer is explaining something, the user has to watch
the screen and listen. And when that person has to make some
assignments he has to keep his attention on the assignment; e.g.
reading the screen, and typing and using the mouse, as long as
he is focused on the task.
First the face has to be located. In [BLF03] the authors describe
a fast method to detect faces at real time, and they use Gabor
wavelets to find facial expressions. After that they classify the
facial expressions with SVM’s (Support Vector Machines) and
the help of Adaboost.
When the so-called ‘focus of attention’ of the user is not on the
computer anymore, the system can react on this information. It
can warn the user to keep his or her attention on the program, it
can use flashy animations and sounds to attract the user’s
attention, or it can change the information to a (hopefully) more
interesting subject.
To start, what exactly is a tutoring system? According to
[WTS06] an Intelligent Tutoring System (ITS) is a system that
helps a student with a certain topic. This help includes lecturing,
giving tasks, asking questions, explaining things and giving
feedback.
This paper will describe all the aspects needed for such a
system. The next chapter will give some basic background, and
Chapter 3 will explain why detection of a loss of attention is
useful for a tutoring system and what such a system can do with
this information. Chapter 4 is about recognizing facial
Gabor wavelets are used a lot in the recognition and
classification of facial expressions. For example [LAK98],
[ZS04] and [DW05] all use Gabor wavelets to extract facial
expressions.
3. INTELLIGENT TUTORING SYSTEMS
In [GVR01] the authors created an ITS that can teach the
fundamentals of computer hardware, the operating system and
the internet to students. The interface consists of a talking head
to act as a dialogue partner, and figures and animations. In the
dialogue between the system and the student the system will ask
questions which the student has to answer. The ITS can give
feedback to the user, and can explain things when the student
does not understand a particular piece of information.
In [ZHE99] the authors made an ITS for medical students. In
this system a physiological change is presented to the student,
after which he or she has to predict the effects on seven
important physiological parameters like heart rate, stroke
volume, etc. When the student made these predictions, the
system will start a dialogue with him to correct the errors in
these expectations.
In these two examples letting the student do the work is very
important. When the student is finished, the ITS can give
feedback on the work and explain parts of the topic that are not
clear yet.
4. WHY DISTRACTION DETECTION
These Tutoring Systems can really help, but a problem can arise
if the student is not paying attention to the system. For example,
the ITS is explaining something or giving feedback on a task,
but the student is talking with his neighbour. Or the student is
supposed to do a task, but instead he is fiddling with a pencil.
Or maybe the system just asked a question and is waiting for an
answer while the student is doing something else. In these
situations it is useful for an ITS to know that the student is
distracted. When it knows that, it can try to do something about
it.
4.1 What to do with distracted students
When the student has lost his attention from the desired task or
object, there are several things to do.
•
•
Warn the user. The ITS can warn the user that his
attention is not on the desired task or object, and that
he or she should return to that task or object. But this
method will only work if the student obligated to
work with the system. People do not like to be
commanded by a computer, and if they do not like the
system anymore they can just walk away.
Ask the user to return. If the student is voluntarily
working with the system, or if the ITS just is polite,
then the system can ask the student to return to the
task.
•
Ask the user what is wrong. If the student has his
attention on something else, then maybe there is
something wrong. Maybe another person is asking
him a question, or maybe he has some problems with
the task. If the system asks what is wrong then it can
do something with that information. It can wait until
the student answered the question, or it can give him
extra help with the task.
•
‘Silently’ asking for attention. Instead of trying to
communicate with the student, the ITS can also try to
get the attention with auditory and visual signals. It
can give a small beep so the user knows the system is
waiting, or it can put some flashing object on the
screen. This method (if implemented subtlety) is not
as intrusive as asking or commanding the student to
return to the task, but now the ITS is only giving
signals. The decision is totally with the student.
•
Stop. If the student is constantly focussing on
something else the system can decide to just stop to
help the student. But this is a harsh method with the
danger of stopping the program when the student did
not do anything wrong (e.g. after several false positive
distraction warnings).
•
Pause. Instead of stopping the entire program, the
program can also pause its current activity until the
student focuses back on the ITS.
•
Just continue. Instead of trying to get the attention of
the student, the tutoring system can just continue with
its current activity. This may work if the student is
just thinking a bit about a task, or if he is answering a
question while the system is waiting for response, but
since a tutoring system has to help a student it is not
recommended to neglect information like a loss of
attention.
These are a lot of choices, and most of them depend on a lot of
variables, externally and internally. There is no best solution,
although good solutions probably use several methods,
depending on the situation.
5. THE SYSTEM
So, the ITS should be able to detect when a student is distracted
using facial expressions and gaze direction. This will result in a
system like the one in Figure 1. This system consists of the ITS,
two subsystems (to detect gaze direction and facial expressions)
Gaze direction
ITS
Collaborative state
Facial expression
Processor
Distraction
Figure 1: a simple model of the system
and a processing unit. This unit will be provided with data from
the two subsystems and the collaborative state from the ITS,
and this data will result in an output that tells if the current
student is distracted or not. In the next three chapters the two
subsystems and the collaborative states from the ITS will be
treated.
6. RECOGNIZING FACIAL
EXPRESSIONS
For a system that has to detect a loss of attention it might be
very useful to detect and recognize facial expressions of the
current user. For example, if the user looks sad or angry, he or
she is probably thinking about something else and the computer
might ask what is wrong. Or if the user looks away from the
screen for a second, there is nothing wrong. But when that same
user looks away for a second and suddenly starts laughing, then
again he or she is probably distracted [SHF03].
So, recognizing facial expressions is probably something the
computer will use. But in order to explain how this recognition
works, we have to go to the basics: wavelets.
6.1 Wavelets
If you have a certain signal or wave, for example a sound wave,
it is very costly to store and keep every detail of the signal. A
better approach is to find a mathematical formula that describes
the signal. An example of this approach is the Fourier transform
[WFT06]. This method tries to “re-express the signal into a
function in terms of sinusoidal basis functions.” That means that
it tries to find a function that is the sum or integral of sinusoidal
functions multiplied by some coefficients, in such a way that it
has to approach the original signal as close as possible.
The problem with this function is that a change in the function
affects the whole sample, so this method can not express local
minima or maxima. Another, better approach is to use wavelets.
Figure 2: Three example Mother wavelets [WWA06]
These transformations do not only use spatial information, but
also time. It will cut the sample in several pieces, and the result
will be a series of wavelets, all for another time frame
[WWA06]. To generate the wavelets a very simple wavelet is
used. This is called the ‘mother wavelet’. Examples of the most
commonly used mother wavelets are shown in Figure 2. These
mother wavelets will be transformed, translated and changed
until it matches the original sample as much as possible.
6.2 Gabor Filters
Gabor wavelets are a more complex form of normal wavelets.
These wavelets are composed of two part: a complex sinusoidal
carrier and a Gaussian envelope to put that carrier in [CAR06].
In other words, the wavelet takes the form g(x,y)=s(x,y)w(x,y).
s(x,y) = exp(i(2π(u0x+v0y)+P)). This is the formula for the
carrier. The Gaussian envelope is just an ordinary Gaussian
normal function.
These Gabor wavelets can be used as a filter for images. This
results in a set of wavelets, and this set can be used to extract
certain features from the image, for example edges.
An example of this feature extraction is in Figure 3. The upper
row shows the Gabor filters that were used, and the two rows
below that are the results of those three filters on two different
facial expressions. These results contain less information
because only the most important features are stored. Now they
can be used for analysis and recognition.
6.3 Using the Data
Now that the facial images went through the Gabor filters, a
new image of the face is available in which only the most
important features are stored. This image now has to be used for
facial expression detection. This can be done in various ways,
for example using Principal Component Analysis, but this
section will describe to approaches that have a better
performance.
6.3.1 Support Vector Machines
This approach is used in [BLF03]. They trained seven SVM’s,
each one to distinguish an expression in the following list:
happiness, sadness, surprise, disgust, fear, anger, and neutral.
Each SVM was trained for one of the expressions, and when a
Gabor image is fed to a SVM it will give the likelihood of that
image belonging to that expression. So, to recognize a facial
expression the associated gabor filtered image is put into the
seven SVM’s, and the expression that belongs to the SVM that
returns the highest likelihood value is chosen.
The article also uses AdaBoost (short for Adaptive Boosting).
This algorithm uses the same training set over and over and
only needs a small dataset. They combined SVM’s with
AdaBoost and got even better results than normal SVM’s.
6.3.2 Convolutional Neural Networks
In this method a Convolutional Neural Network (CNN) is used
to learn the differences between the Gabor images [FAS02].
Convolutional Neural Networks are a special kind of multilayered neural networks [LEC06]. They are trained with a
version of the back-propagation algorithm, just like normal
neural networks, but their architecture is the difference. They
are designed ‘to recognize visual patterns directly from pixel
images with minimal preprocessing’. As with the Support
Vector Machines, there will be 7 CNN’s, each one trained for
one specific expression.
7. GAZE DIRECTION
The detection and recognition of facial expressions may be
useful, but another great cue to detect distraction is the gaze
direction. If the user is not looking at the screen or any other
object used in the session then there is a good chance that the
user is doing or thinking about something else. To make it
easier we assume that the people working with the system do
not need extra items besides the computer, because then the
system should also detect if the user is looking at that item or at
something else.
The problem is that it is very hard to detect what the user is
actually looking at, because the direction of the eyes is hard to
track [UKS04]. Instead, it is more easy to detect the direction of
the head. In most of the cases these directions will be the same.
To detect the head orientation, first the program needs a face.
To do this it will use a Six-Segmented-Rectangular filter (SSR
filter). The result are several rectangulars which are divided in
six segments (see figure 3). Such a rectangle is a Face candidate
if it has an eye and an eyebrow in segment S1 and S3. So, it
must at least satisfy the following two conditions: ‘aS1 < aS2
Figure 3: examples of responses of Gabor filters [LAK98]
and aS1 < aS4’ and ‘aS3 < aS2 and aS3 < aS6’. In these
formulas ‘aSx’ is the average pixel value of segment x.
•
When the Face candidates are computed a Support Vector
Machine (SVM) is used to determine which Face candidates are
real faces. A typical face pattern that is used in the SVM is in
figure 4.
7.1 Tracking the eyes
Next the eyes have to be found. Finding the eyes is relatively
easy (local minima), but tracking (e.g. following) them is
another
story.
Blinking can greatly
disrupt the pattern and
the
updating
templates will get
lost. To solve this
problem, instead of
tracking the eyes a
Figure 4: The six segments of a SSR
“between-the-eyes”
filter
tracking template is
used [GOR02]. This
template tracks the point
between the eyes, which is
a bright spot (the bridge of
the nose), with two dark
spots (the eyes) on both
sides.
With
template
matching the positions of
Figure 5: A typical face pattern
the eyes can know be
tracked.
7.2 Tracking the nose
After locating the eyes, finding the nose is fairly easy. The tip of
the nose is a bright area, and can be found below the eyes. Once
the tip of the nose is found it can be tracked. This is done by
searching every frame for the brightest spot in the locality of the
previous location of the nose.
7.3 Face orientation
Now that the locations of the eyes and the nose are known, the
direction of the head can be detected. Normally, multiple
cameras are necessary for a 3D-picture so depth-distances can
be measured. But this system uses only one camera.
In general, on a picture of a frontal face, the nose is on a vertical
line exactly between the eyes. However, when the face rotates
this line will move closer to one eye (distance q), and further
away from the other (distance p). The ratio of these distances
can be used to calculate the rotation, using the following
formula:
r = q / (p+q) – 0.5
The sign of r means the direction of the rotation (left of right).
The advantages of this method are that it only requires one
camera, and it is fast. The face orientation can be calculated in
real-time because the time between the frames is all it needs.
8. COLLABORATIVE STATES
Knowing the facial expressions and the head direction of the
user is important for distraction detection, but it is not enough.
For example, when the user has to answer a question, he has to
focus on the keyboard to type the answer. But when the system
is explaining something, then the user should not look at the
keyboard because then he cannot see the program.
The problem here is that the distraction is dependent of their
collaborative state, i.e. in what kind of state are the current
activities of the system and the user. Different states require
different forms of attention. These states are:
•
•
•
Explaining. In this state the system is explaining or
teaching the user something. This can be done in
several ways. For example, there may be text on the
screen, or there may be an animation. In any case,
usually there is something on the screen that requires
attention. This means that if the system is in this state
then the focus of the user should be on the screen.
Working task. In this state the system has given the
user a task to do. In this case this is a ‘working task’
which means that it is a task that requires more work
and less thinking. Most of the time the user will
switch his attention between the screen (to read
things) and the input devices. Things get more
complicated when the user has extra material next to
him, but to simplify things we will assume that the
system with its input devices is all that the user needs,
and that the user should have his attention on the
screen or the input devices.
Thinking task. In this task for the user the thinkingpart is more important than the working-part. Users
have to think a lot to complete the task. This means
that the users will not have their attention on the
screen or the input devices all the time. Maybe they
stare out of the window trying to come up with a good
solution. In this part facial expression can really help,
because it must be clear that the user is really thinking
about the task.
Pause. In this state the user notified the system that he
wants a break or something like that, and the attention
of the user can be on anything.
9. PROCESSING THE DATA
Now that the subsystems have collected the necessary data, it is
time to add all the data together and try to find out if the student
is distracted. This will be done in the Processor-unit (see figure
1).
But data from one separate moment is not enough. You can not
say anything if you know that the user looked away at one
specific time; maybe it was only for one second. That is why
data over time has to be analyzed. This time will be the last X
seconds, where X has to be determined by tests and is
dependent of the environment and the tasks.
This results in the following input in the Processor:
•
The current collaborative state.
•
The gaze directions of the last X seconds.
•
The facial expressions of the last X seconds.
From this data the Processor must now say if the student is
distracted or not (or give a certain chance that the student is
distracted). The most logical choice would be to include some
kind of learning system that can deal with the input. An
important aspect in these systems is that the data is over time.
The following learning methods are probably able to deal with
the data [ALP04]:
•
Hidden Markov Models (HMM’s). These are models
in which the states are not observable. But every state
emits observations with a certain chance, and these
observations can be seen. The structure of the model
however is unknown. Every state also has transitions
to other states. These transitions also have a certain
chance.
HMM’s are often used in linguistics. In speech
recognition HMM’s are used to find words in series of
phonemes. That is exactly the strong point of HMM’s:
it is great in series of observation, so the data over
time the Processor receives should not be a problem.
•
Time Delay Neural Networks (TDNN’s). Normal
Neural Networks (NN) consists of perceptrons, small
basic processing units. With these perceptrons the NN
gives a weight to each one of the input variables. In
the training phase the output of the NN is compared to
the real output, and the weights of each of the input
variables are changed accordingly. In the end
(hopefully) a model with weight factors exists that can
predict the correct output.
But a normal Neural Network cannot handle data over
time. In Time Delay Neural Networks previous inputs
are delayed. After a certain amount of delays all the
data (so including the data from the past) is fed to the
TDNN as one group of data. The only limitation is
that the time window has to be determined
beforehand.
•
Recurrent Neural Networks (RNN’s): In Recurrent
Neural Networks the network does not only consist of
feedforward connection, but also has self-connections
and connections to units in the previous layers. This
recurrency gives the model a short-time memory,
which can be used to handle the data over time the
Processor receives. The limitation here is that training
a RNN is very complex and requires large amounts of
space.
These are three possible solutions for the learning system.
It is very hard to say beforehand which solution is the best,
although the HMM is probably the most simple model.
10. EXAMPLE SITUATIONS
This paper is mainly about using facial expressions to recognize
a loss of attention. Normally, only the gaze direction is used to
detect if a person is distracted and looking at something else
than he or she should [UKM04]. So why add facial expression
detection? It adds a lot of extra computational time and makes
the detection program a lot more complex.
To make clear that facial expressions can help, several examples
of distraction are given below. There are also examples of
detecting distraction in general (so not specifically with facial
expressions).
•
•
Looking away. If the system is in the Explaining state
or in the Working task state and the user is looking
away, then there is a very good chance that the user is
distracted. In those collaborative states it is required
that the user has his focus of attention at the screen or
the input devices. But the problem is that you cannot
expect a user to look at those two objects all the time.
People look at other objects once in a while, maybe to
relax their eyes for several seconds, or because they
suddenly see something. This does not always mean
that the user is distracted. However, there are other
cues that can help in this situation, for example time.
It is no big deal if the user looks away for several
seconds, but if it takes too long then the person is
probably not paying attention. Another cue is what his
current focus of attention is. If that is a fixed object
(i.e. his head orientation stays the same) there is a
chance he is just relaxing his eyes (more on this point
later), but if the user is looking around and focussing
on everything except the screen and the input devices
then he is probably bored and distracted.
Looking at a fixed object. A user looking at a fixed
object that is not the screen or any input device can
also be distracted (we already assumed that there will
not be extra items to work with), but maybe it is just a
minor distraction that does not really matter to the
system. In this case facial expressions can help. If the
user has a neutral face then it is hard to detect
anything. But if the user has a very powerful facial
expression (e.g. a large grin, surprise, anger, etc) then
there is probably something else happening that is
distracting him. With a neutral face he could just be
thinking about the assignment (this will probably
happen a lot in the Thinking task state of the system).
•
Talking. When detecting facial expressions it is very
easy to detect if the user is talking at the same time (a
lot of different mouth movements in a short time
interval). This is a great cue to detect loss of attention.
The user can look at the screen and at the same time
talk to a person standing next to him while the
computer system is explaining something. Then the
user is clearly not paying attention. Of course this can
also be detected with a microphone, but in a crowded
environment the system does not know who is talking.
These are just some examples of situation in which the system
can detect distraction. The system proposed in this paper has to
learn these cases by itself and this can vary per system and
environment.
11. DISCUSSION
There are two major concerns about this system. One, it is only
theoretical. It has not been build, and there is no absolute
evidence that it will work as it should. However, I think that
everything is explained well enough and that enough arguments
have been given to make it plausible enough that the system
will probably work.
The second major concern is that the two subsystems, one for
gaze direction and one for facial expressions, are only tested
inside laboratories (to my knowledge). This means that the
quality of the subsystems is not guaranteed in real situations.
But the given solutions are not the only available solutions. If
another method to detect facial expressions or gaze direction is
available that works better than the one given in this paper, it is
very easy to substitute that method in this system. The methods
given here are just recent methods that are very promising.
These are the two major concerns about this system. However,
this does not mean that besides these concerns the system is
perfectly sound. Maybe facial expression do not contribute to
the quality of distraction recognition that much. And maybe
there are more easy methods to detect a loss of attention. But
more things can be said when the system is actually build.
12. FUTURE WORK
Of course the first thing to do is to implement this system to see
how it performs. Depending on the results, several
improvements can be made. The quality of the subsystems
should be tested and if they are not good enough they should be
replaced by better methods.
After the implementation the best learning method should be
tested too. This paper gives several learning methods, but it is
very hard to predict which method works best. This should be
tested.
CONCLUSIONS
This paper should give a very clear idea of what a distraction
detection system in an Intelligent Tutoring System consists of.
Facial expressions are detected using one camera and Gabor
filters, and the expressions are recognized by Support Vector
Machines. Gaze direction is detected in real-time by also one
camera and simple geometry. This data plus the collaborative
state in which the system and the user are is fed to a learning
system which will tell the ITS if the student is distracted or not.
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