COMPETITIVE AND COOPERATIVE SOLUTIONS FOR

REMOTE EYE-TRACKING

Giovanni Crisafulli, Giancarlo Iannizzotto and Francesco La Rosa

Visilab, Faculty of Engineering, University of Messina, Contrada Di Dio (S. Agata), Messina, Italy

Keywords: Eye-tracking, Image analysis, Competitive approach, Cooperative approach.

Abstract: Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. In this

paper, we present an innovative approach to the problem of the eye-tracking. Traditional eye-detectors,

chosen for own properties, are combined by two different schemes (competitive and cooperative scheme) to

improve own robustness and reliability. To illustrate our work, we introduce a proof-of-concept single

camera remote eye-tracker and discuss its implementation and the obtained experimental results.

1 INTRODUCTION

Eye Tracking (ET) is the process of measuring eye

positions and eye movements in a sequence of

images (Ji and Zhu, 2004), (Morimoto et al., 1998b).

Specifically, detection and tracking of the iris or

pupil can be used to infer the direction of interest of

the human subject, this is denoted gaze (Matsumoto

and Zelinsky, 2000).

By tracking the eye-gaze of a user, valuable

insight may be gained into what the user is thinking

of doing, resulting in more intuitive interfaces and

the ability to react to the users’ intentions rather than

explicit commands (Jacob, 1991), (Morimoto and

Mimica, 2005).

Also, gaze can play a role in understanding the

emotional state for humans, syntesizing emotions,

and for estimation of attentional state. Specific

applications include devices for the disabled, e.g.,

using gaze as a replacement for a mouse and driver

awareness monitoring to improve traffic safety

(Coifman et al., 1998).

Early eye-tracker (and eye gaze tracker) were

developed for scientific exploration in controlled

environments or laboratories. Eye gaze tracking

(EGT) data have been used in ophthalmology,

neurology, psychology, and related areas to study

oculomotor characteristics and abnormalities, and

their relation to cognition and mental states.

Successful attempts are still limited to military

applications and the development of interfaces for

people with disabilities.

To be applied in general computer interfaces, an

ideal eye tracker should be accurate, reliable, robust

(should work under different conditions, such as

indoors and outdoors, for people with glasses and

contact lenses, etc), non-intrusive, allow for free

head motion, not require calibration and to have

real-time response.

Our work is concerned with the usability, the

reliability and the robustness of eye-trackers for

general applications. As a proof-of-concept, we

propose a single camera remote eye-tracker that use

two different schemes (competitive and cooperative)

to merge (Freund and Schapire, 1997) the results of

some eye-detectors. An improving of the robustness

and reliability is obtained.

The paper is organized as follows. In Section 2

we describe the main ET approaches to be found in

the literature. In sections 3 we describe the system

we propose. In Section 4, we give details of the

experimental results obtained. Finally, in Section 5,

we present our conclusions and some indications of

future development.

2 RELATED WORKS

Detection of the human eyes is a difficult task due to

a weak contrast between the eye and the surrounding

skin. As a consequence, many traditional techniques

for ET and EGT are intrusive, i.e., they require some

equipment to be put in physical contact with the

user. These techniques include, for example, contact

521

Crisafulli G., Iannizzotto G. and La Rosa F. (2009).

COMPETITIVE AND COOPERATIVE SOLUTIONS FOR REMOTE EYE-TRACKING .

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pages 521-528

DOI: 10.5220/0001555105210528

 SciTePress

lenses, electrodes, and head mounted devices

(Morimoto and Mimica, 2005). Non-intrusive

techniques (or remote techniques) are mostly vision

based (Ji and Zhu, 2004), (Morimoto et al., 1998a),

(Hsu et al., 2002), i.e., they use cameras to capture

images of the eye. Some camera-based techniques

might be somewhat intrusive if they require to be

head mounted (Babcock and Pelz, 2004), (Li et al.,

2005), (Li et al., 2006).

For diagnostic applications, where eye data can

be recorded during a short experiment and processed

later, the time required to setup the eye tracker and

the discomfort that the equipment might cause do

not constitute a problem. This is also true for a few

interactive applications where the user has to depend

heavily on the eye tracker to accomplish some task

(i.e., there is little choice or no alternative device).

A remote eye tracker (RET) offers comfort of

use, and easier and faster setup, allowing the user to

use the system for longer periods than intrusive

techniques. Although the accuracy of RETs is in

general lower than intrusive ETs, they are more

appropriate for use during long periods. The pupil–

corneal reflection technique (Zhu and Ji, 2005),

(Morimoto et al., 1998a) is commonly advertised as

a remote tracking system that is robust to some head

motion.

Camera-based EGT techniques rely on some

properties or characteristics of the eye that can be

detected and tracked by a camera or other optical or

photosensitive device. Most of these techniques have

the potential to be implemented in a non-intrusive

way.

The limbus and the pupil are common features

used for tracking (Daugman, 1993), (Haro et al.,

2000). Limbus is the boundary between the sclera

and the iris. Due to the contrast of these two regions,

it can be easily tracked horizontally, but because the

eyelids in general cover part of the iris, limbus

tracking techniques have low vertical accuracy. The

pupils are harder to detect and track because of the

lower contrast between the pupil–iris boundary, but

pupil tracking techniques have better accuracy since

they are not covered by the eyelids (except during

blinking).

To enhance the contrast between the pupil and

the iris, many eye trackers use an infrared (IR) light

source (Zhu and Ji, 2005), (Morimoto et al., 1998a) .

Because IR is not visible, the light does not distract

the user.

Sometimes, the IR source is placed near the

optical axis of the camera. Because the camera now

is able to ‘‘see’’ the light reflected from the back of

the eye, similar to the red eye effect in night

photography using a bright flash light, the camera

sees a bright pupil instead of a regular dark pupil.

The light source can also generates a corneal

reflection (CR) or glint on the cornea surface, near

the pupil. This glint is used as a reference point (Li

et al., 2005) in the pupil–corneal reflection technique

for EGT.

Due to the use of active IR lighting, this

technique works better indoors and even in the dark,

but might not be appropriate outdoors, because

sunlight contains IR and the pupils become smaller

in bright environments.

The literature offers several techniques for

detecting eyes directly (Kawato and Tetsutani,

2002a; 2002b), or as a sub-feature of the face (Hsu

et al., 2002). Faces can be detected from background

subtraction, skin color segmentation, geometric

models (Li et al., 2005) and templates (Matsumoto

and Zelinsky, 2000), artificial neural networks (Ji

and Zhu, 2004), etc.

Direct methods for eye detection use spatial and

temporal information to detect the location of the

eyes.

Their process starts by selecting a pool of

potential candidates using gradient fields and then

heuristic rules and a large temporal support are used

to filter erroneous pupil candidates.

The use of a support vector machine (SVM)

avoids falsely identifying a bright region as a pupil:

the pupil candidates are validated using SVM (Zhu

and Ji, 2005) to remove spurious candidates.

Instead of using explicit geometric features such

as the contours of the limbus or the pupil, an

alternative approach is to treat an image as a point in

a high-dimensional space. Techniques using this

representation are often referred to as being

appearance-based or view-based (Black and Jepson,

1998).

In (Haro et al., 2000) applies a linear principal

component analysis approach to find the principal

components of the training eye patches. In (King and

Xu, 1997) use a probabilistic principal component

analysis to model off-line the intra-class variability

within the eye space and the non-eye distribution

where the probability is used as a measure of

confidence about the classification decision.

The method proposed in (Ji and Zhu, 2004) also

does not use explicit geometric features. They

describe an EGTer based on artificial neural

networks (ANN). Once the eye is detected, the

image of the eyes is cropped and used as input to a

ANN. Training images are taken when the user is

looking at a specific point on a computer monitor.

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

522

Finally, when some pupils are detected, the

information about its position and velocity can be

passed to a tracker module (Shi and Tomasi, 1994),

(Bouguet, 1999) to enforce motion tracking stability.

Kalman filtering is often used to predict pupils

position in current frame, therefore greatly limiting

the search space. Sometimes, Kalman filtering can

be improved by mean-shift tracking (Ji and Zhu,

2004), which tracks an object based on its intensity

distribution.

3 SYSTEM

In this paper we propose an innovative passive

remote ET system that uses hardware off-the-shelf,

whose performances are independent from the

lighting conditions (natural or artificial).

Our ET system uses several ET techniques

without the needs of any initial calibration. An

advantage from Optical Flow (Tomasi and Kanade,

1992) has been taken.

The main contribute of our work is to obtain an

accurate and robust estimate of eye-position, using

some eye-detection techniques described later,

according to two different schemes, called in this

paper competitive and cooperative scheme.

In the first scheme, frame by frame, it’s

estimated the reliability of each technique and then

it’s chosen which one to use. In the second scheme,

for each frame, all techniques contribute to the

determination of the final results.

To develop the schemes, we analyzed the

different performances of each eye-detection

technique according to the operating conditions:

lighting conditions, dynamic of the user, partial iris

occlusions, and distance of the user from the camera.

Figure 1: The main operational phases of the system.

As shown in Figure 1, each frame is elaborated

by a face detector. It returns, eventually, a Region

Of Interest (ROI) that encloses the face of the

“observed” user. The region returned from the face

detector is elaborated to define two Eye-ROI (E-

ROI) that will enclose the eyes of the user. The face

detection is obtained using a boosted classifier

(Freund and Schapire, 1996) based on Haar-like

features (Viola and Jones, 2001) and improved in

(Lienhart and Maydt, 2002).

To select the E-ROI we have tried to use the

method proposed in (Peng et al., 2005), but in

according to the measures that we have realized, we

use some parameters determined empirically.

Figure 2: Eye-ROI definition.

Let L be the side length of the Face-ROI, the

anchor point of the E-ROI on left will have the

following coordinates (a,c), referred to the top-left

of Face-ROI. The E-ROI will have a width equal to

w and a height equal to h. In our system, we use

a=L/6, c=L/3.6, w= L/2.8 and h=L/3.7.

The same size, but with a different anchor point,

is used for the other E-ROI (see Figure 2).

Also in our system, the choice of the number of

detection techniques adopted is exclusively limited

by the available computational resources. We use six

eye-detection techniques, selected for them

complementary properties (Section 4).

A brief description of the detection algorithms

adopted is presented in the following section.

3.1 Eye-Detection

The first eye detection technique that we use is

described in (Daugman, 1993) and was used,

originally, as pre-processing step into an iris

recognition system.

The algorithm returns the coordinates of pupil

centre. Daugman asserts that the technique is

optimum (it will certainly find the optimum contour)

but it is computationally expensive, because the

number of contours elaborated depends on the

analyzed region size. This technique offers the

further advantage to be robust respect to iris

occlusions due to the overlap of eyelid on iris.

On the other hand, when the user wear glasses

the accuracy results is considerably lower than in

normal operating conditions.

The second technique is proposed in (Hsu et al.,

2002). In this case, the detection of eyes is obtained

processing the acquired frame in the YCbCr color

space. The original algorithm was modified to work

with E-ROI.

COMPETITIVE AND COOPERATIVE SOLUTIONS FOR REMOTE EYE-TRACKING

523

An EyeMap is obtained processing the E-ROI in

the YCbCr space. In EyeMap, pixels around the eye

are characterized by high value. Finally, the EyeMap

is processed with a Dilation operator and then

thresholded. After the extraction of the Connected

Components (CC) in the EyeMap, the system

calculates the barycentre of the one with the greatest

area (and bounding box with width/height ratio

between 0,6 and 1,4).

The calculated barycentre is an estimate of the

eye-position for this technique.

This technique, in good and uniform lighting

conditions, has proved to be robust and accurate.

Performance doesn’t vary significantly raising

distance from camera (in a maximum distance of

60cm). Glasses reduce the performances for this

technique too. False positives and/or false negatives

can be returned by this technique, especially with

noise and shadows

The third technique process the E-ROI into

HSV space.

In this case we take advantage of the high

saturation in light reflection by the pupil. The pupil

is characterized by low value in RGB space, and,

with natural lighting conditions, it can reflect light

with high saturation

In order to extract the pupil centre the input E-

ROI histogram is equalized. We use the maximum in

the saturation image to threshold it with threshold

value T

. Now we extract the Connected

Components (CC) as before.

The technique has shown good results and

responsively in acceptable lighting conditions also

when the user is moving.

The detection technique presents good results

also when the user wears glasses. It can obtain

detection errors with a cluttered background into E-

ROI.

The fourth technique uses the intensity of the

light reflected by the pupil. That, in fact, is a

minimum of brightness into E-ROI.

Analyzing red channel we reduce the shadow

presents into E-ROI. Background, eyebrows and

glasses with dark frame, if enclosed into E-ROI, are

the principal “obstacles” for this technique.

The fifth technique use two convolution masks

customly designed for eye detection. These were

defined using typical circular shape and

characteristic gray level in the iris-sclera complex.

In our implementation we used the masks shown

in eq. 1

221

113

221

−

−−

−

122

311

122

−

−−

−

(1)

A schematic representation of this technique is

shown in Figure 3.

Figure 3: The main operational phases of fifth technique.

Glasses don’t modify the technique’s

performances, also with dark frame. The detection

isn’t affected from shadows in region close to eye.

The method proposed to find eyes doesn’t guarantee

the desired continuity and precision. There are cases

in which the resultant mask has a single large CC,

almost equal to E-ROI themselves. Instead it can’t

locate CC when there are excessive movements or

reflections. This technique has a decrease of

performances in the presence of cluttered

background.

The latest and sixth technique used for eye

detection is based on global relation between pixels

of image. We take advantage of Hough Transform

(Gonzalez and Woods, 2002) to extract

circumferences into E-ROI previously elaborated by

some mathematical morphology operators.

The circumferences that we search are the pupil

and iris. Iris and pupil detection is obtained applying

the Hough Transform to the edges detected by

Canny’s Operator (Canny, 1986).

Hough presents precision and accuracy that

don’t vary significantly raising distance from camera

(max 60cm). On the other hand, in case of bad

lighting operating conditions, Hough Transform can

return high number of false positives or no response.

As for Daugman technique it needs considerable

computational resources so it limits the number of

techniques that can be used.

Finally, its performances decrease highly with

sudden movements of the user.

3.2 Competitive and Cooperative

Schemes

Now we describe the schemes that we use to merge

the results obtained by the single adopted

techniques. Each technique returns an estimate

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

524

(Xi=(x

) with i=1,2,…,N) of the eye position that

is the barycentre of the pupil into image. This is the

core of our system, and we’ll try to demonstrate that

cooperative and competitive schemes can return

more accurate result than the described techniques.

Competitive scheme aim to determine online

which technique used offers the better results. After

few frames, our system will be, already, capable to

establish which technique is the most reliable.

The approach’s peculiarity is that we consider

possible a variation of operating conditions at any

time. In fact, when the operating conditions of the

system change, we can use, again, the techniques

that before was considered not reliable. This

peculiarity is capital when, for example, lighting

operating conditions and/or distance of the user from

the camera change. Also the camera can change

automatically some its setup (Automatic Gain

Control, Auto Iris, Back Light Compensation,

Exposure); we used low cost cameras.

The competitive scheme takes advantage of three

principal information: the set of points returned from

the techniques, the “hit” probabilities of the

techniques and the estimated point (X*=(x*,y*))

obtained by the Optical Flow (OF) (Tomasi and

Kanade, 1992). OF is used in our system to provide,

using historical results, a prediction useful in

processing of the next frame.

First of all, we define a matrix of the distances D

(see eq. 2). The elements of D are the relative

distances, expressed in pixel, between the points

returned from the eye-detection techniques.

221

112

…

…………

…

(2)

D is a symmetric NxN matrix (in our case N=6).

We now introduce the proximity vector V:



(3)

The elements v

quantify the number of

techniques that return a point near to the estimate of

technique, depending from the elements of D,

according to the eq. 4 and eq. 5.

∑

iji

(4)

with

⎩

⎨

⎧

<≠

otherwise 0

5 and if 1

dji

(5)

Then we define the overlap vector S:



(6)

with

⎪

⎩

⎪

⎨

⎧

≠>⋅

∑

otherwise 0

and 0 v

jid

ijij

(7)

value will be directly proportional to the

distances

and inversely proportional to v

. In our

purpose, overlap vector is an estimate of the

techniques reliability. In fact, we consider

reasonable that a measure is as more reliable as more

similar to the other estimates obtained.

It remains to choose the techniques that

presumably have correctly responded. We consider

reliable the techniques with

equal to the minimum

value of

S (s

min

). This first analysis allows to

deducing that one or more eye detection techniques

can be considered reliable. This information will be

used by them for a transitional period chosen equal

M frames. After the first M frames we enrich our

processing considering the historical results (in our

implementation

M is equal to 20). To consider the

historical behavior of the system, a counter (

) is

associated to each technique and we increase his

value at each hit (

min

). The value of the counter

can be used to determine a prior probability (see eq.

8) related to the reliability of the obtained

estimation. The described method is summarized in

Table 1

∑

with Ni ,...,1=

(8)

Finally we sum the prior probabilities (see eq. 9

and eq. 10) of the techniques with minimum

∑

δp=p* with

Ni ,...,1=

(9)

⎩

⎨

⎧

otherwise 0

s if 1

mini

(10)

If p* is bigger than a prefixed threshold (p*>T),

then we select the point returned by most probable

technique, among techniques that have s

min

COMPETITIVE AND COOPERATIVE SOLUTIONS FOR REMOTE EYE-TRACKING

525

On the contrary case, we chose between

X* and

the technique which return closest result to this.

In our experiments we chosen a threshold T

equal to 0,6.

In the latest case, we preferred to take as final

result one output of the eye detection method

described before, only if that is distant less than K

pixel (in our measures K=5) from

X*.

After the first M frames:

 If p*≥T is returned the result of the technique

with maximum p

(and with distance from X*

less than K);

 If p*<T, and exist at least one X

with distance

from

X* less than K pixel, is returned the

result offered from the technique that is

nearest to

X*;

 If p*<T and doesn’t exist at least one X

with

distance from

X* less than K pixel, we discard

all results and return

X*;

After M frames, we also use different increasing

method for the counters n

In the first case, the counters will be increased

for each technique with s

min

In the second case, the counter of the successful

technique will be increased.

In the third case, we won’t increase any counter.

Now it is spontaneous to assign to this kind of

approach the appellation of adaptive.

In fact, as more the playing techniques will cover

several cases as more the system will reply

precisely, robustly and with accuracy.

However in the competitive scheme the final

result can depend from different techniques in

different frames.

Cooperative approach, instead, take advantage

from the efforts of the all techniques that give results

using a particular evaluation criteria.

It’s based on two operations:

 Barycentre calculus of all points (see eq. 11);

 Estimation of new barycentre under 2σ

hypothesis.

()

⎟

⎠

⎞

⎜

⎝

⎛

≡≡

∑∑

,,B

(11)

Where n is the number of technique that returns

a result.

After that is determined the standard deviation σ:

=σ

∑

−

(12)

All the points returned by the techniques that are

less than 2σ distant from

B will be used to calculate

the new barycentre that will be the output of the

cooperative scheme. We can assert that this

algorithm aspires to eliminate the outliers to improve

the accuracy results.

4 EXPERIMENTAL RESULTS

The system was tested during and after development

by several users for a considerable number of hours

in numerous environments with different external

lighting conditions.

To evaluate the performance of the system in

terms of accuracy and repeatability a considerable

number of tests were carried out.

To produce a quantitative evaluation we

compared the output of our system with a ground-

truth reference obtained by manual segmentation of

the video tests.

An estimation of the whole error (due to the

system) can be evaluated from the comparison

between the acquired coordinates and those of the

manual segmentation (ground truth).

Carrying out then a statistical analysis on the

measures we obtained information about the

precision of the system calculating the mean error

and the standard deviation of the error.

Such errors are expressed in pixel or fractions of

pixel.

The measures have been realized asking 5 users

to test 3 times the system using the graphic interface

of the operating system and most commonly used

applications.

Results obtained confirm validity of proposed

solutions and allow an accuracy, precision and

robustness comparing between the schemes and eye-

detection techniques.

In Table 1 are summarized information related to

the operating conditions of the measures, such as

lighting type, number of webcam adopted, eventual

worn glasses and/or shadows.

Table 1: Operating condition in experimental results.

Subject Glasses Webcam

Halogen

lamp

Filament

lamp

1 No 3 Yes

2 Yes 3 Yes

3 No 2 Yes

4 Yes 3

5 No 2

As we said, we have chosen the six techniques

for them complementary properties. A confirm of

this is shown in Table 2 where we can see a

schematic presentation of the principal properties of

the techniques adopted.

BIOSIGNALS 2009 - International Conference on Bio-inspired Systems and Signal Processing

526

Table 2: Principal properties of the techniques adopted. (a:

tolerate movements, b: tolerate glasses, c: independent to

the distance from camera, d: need low computational

resources, e: tolerate occlusions, f: tolerate shadows)

a b c d e f

- n - n y -

y n y - y n

y y n y - n

- n - y y y

n y - - n y

n y y n n n

In Table 3 we present a direct comparison

between the results obtained by the two schemes and

the single eye-detection techniques. We indicated

the competitive scheme with S1 and cooperative

with S2. The single technique (T) is numbered in

according to the order followed in this paper.

For each technique and scheme we report the

mean error and standard deviation in pixels. We

measure the response rate (%) for each technique

and we show the further improvement introduced by

the schemes. Response rate indicate the number of

case in which a technique give a result.

Table 3: Experimental results (E: mean error; σ: standard

deviation; %: response rate).

S1 S2 T1 T2 T3 T4 T5 T6

6,1 6,4 8,8 6,5 8,1 6,7 12,9 11,5

6,3 6,5 8,2 6,0 7,5 6,5 9,2 6,8

98,1 95,8 87,0 72,2 76,4 68,2 59,2 41,4

The results show that the schemes implemented

reduce considerably mean error of the single

techniques. The schemes also provide more

continuity and robustness in offering results,

contrarily to the single techniques.

5 CONCLUSIONS

In this paper, we proposed an innovative approach to

the problem of the Eye-Tracking. Traditional eye-

detectors, chosen for its properties, are merged by

two different schemes (competitive and cooperative

scheme). The described approach features high

reliability and high robustness to noise and bad

illumination. To illustrate our work, we introduced

a proof-of-concept single camera remote eye-tracker

and discussed its implementation and the obtained

experimental results. More applications of the

proposed approach are currently being investigated

in our Lab to portable, handheld and wearable

computers. At the moment, the main issues being

dealt with are computational cost and power

consumption reduction. Finally, we are realizing a

comparative study on a number of (more)

sophisticated different cooperative schemes to obtain

a further improvement in accuracy and reliability.

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