Detection and Characterization of the Sclera

Evaluation of Eye Gestural Reactions to Auditory Stimuli

Alba Fern

andez, Joaquim de Moura, Marcos Ortega and Manuel G. Penedo

Departamento de Computaci

on, Universidade da Coru

na, A Coru

na, Spain

Keywords:

Hearing Assessment, Audiometry, Eye Gestural Reactions, Gaze Movement.

Abstract:

Hearing assessment becomes a challenge for the audiologists when there are severe difﬁculties in the commu-

nication with the patient. This methodology is aimed at facilitating the audiological evaluation of the patient

when cognitive decline, or other communication disorders, complicate the necessary interaction between pa-

tient and audiologist for the proper development of the test. In these cases, the audiologist must focus his

attention on the detection of spontaneous and unconscious reactions that tend to occur in the eye region of

the patient, expressed in most cases as changes in the gaze direction. In this paper, the tracking of the gaze

direction is addressed by the study of the sclera, the white area of the eye. The movement is identiﬁed and

characterized in order to determine whether or not a positive reaction to the auditory stimuli has occurred, so

the hearing of the patients can be correctly assessed.

1 INTRODUCTION

Hearing loss is the third most prevalent chronic health

condition facing older adults (Collins, 1997). The

standard test for the clinical evaluation of hearing

loss is the audiometry (Davis, 1989), a behavioral test

where the hearing thresholds of the patient are eval-

uated in order to diagnose his or her hearing capac-

ity. Since this evaluation is a behavioral test, it re-

quires a high interaction and understanding between

patient and audiologist. This need of communication

is what causes serious difﬁculties when the patients

suffer from cognitive decline or other communication

difﬁculties. A typical interaction is not possible with

this particular group of patients, instead, audiologists

argue that there are some unconscious and sponta-

neous reactions that may correspond with involuntary

signs of perception. These spontaneous reactions are

gestural reactions that, in most cases, are expressed

in the eye region. Changes in the gaze direction or an

exaggerated eye opening might be interpreted as signs

of perception of the auditory stimulus.

The detection and interpretation of these gestural

reactions requires broad experience from the audiolo-

gist. Besides, since the audiologist may pay attention

to the handling of the audiometer while he is trying to

detect these gestural reactions to the stimuli, the eval-

uation requires a high degree of concentration by the

expert and it is very prone to errors.

All these circumstances highlight the need for an

automatic tool which support the audiologist in the

evaluation of patients with cognitive decline. This

method must be focused on the eye region in order

to detect eye gestural reactions to the sound. To that

end, in this proposal we are going to analyze the eye

movements using color information from the sclera,

the white area in the eye. The relation between the

location of the iris and the white distribution of the

sclera allows to determinate the gaze direction. This

relation must be identiﬁed and characterized in order

to determine if a movement has occurred as a reaction

to the auditory stimulus. This is why, the method-

ology will enable the proper assessment of patients

when no interaction is possible.

An alternative solution was proposed in (Fernan-

dez et al., 2013), where the analysis of the eye move-

ments is addressed by the use of the optical ﬂow in

order to detect the movement occurred between two

consecutive images. In this case, we propose the

analysis of the eye movements by the analysis of the

sclera, since the information provided by this area

could be as good as the previous alternative.

In the literature, we can ﬁnd different approaches

that use information about eye movements with dif-

ferent aims. For example, in (Coetzer and Hancke,

2011) a system for monitoring drivers fatigue was

proposed. In (Buscher et al., 2009) the tracking of eye

movements is used in order to study the visual behav-

313

Fernández A., de Moura J., Ortega M. and Penedo M..

Detection and Characterization of the Sclera - Evaluation of Eye Gestural Reactions to Auditory Stimuli.

DOI: 10.5220/0005295603130320

In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISAPP-2015), pages 313-320

ISBN: 978-989-758-090-1

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: Steps of the methodology.

ior of the user while he is browsing the Internet. A

similar aim was proposed in (Yu et al., 2013) where

the eye tracking was analyzed while reading poetry,

this survey allows the researchers to infer information

about how the cognitive and lexical processing affect

reading and learning.

Since we are trying to detect eye movements as-

sociated to unconscious reactions, it is important to

note that each patient may react differently, and even

the same patient may show different reactions dur-

ing the same evaluation. For this reason, we decided

to propose a novel methodology for the eye tracking

which allow the best possible adaptation to the partic-

ular characteristics of our domain.

This paper is organized as follows: Section 2 is

devoted to the description of the methodology. Ex-

perimental results are included in Section 3. Finally,

Section 4 provides some discussion and conclusions.

2 METHODOLOGY

As depicted in the Introduction, the development of

an automatic solution capable of analyzing the eye

movements and detecting gestural reactions to the

stimuli would be very helpful for the hearing assess-

ment of patients with cognitive decline. This auto-

matic solution will receive as input a video sequence

recorded during the development of the hearing as-

sessment, and it is going to be analyzed frame by

frame.

The proposed methodology is divided into the ﬁve

main stages represented in Fig. 1. In the ﬁrst one, we

locate the face within the complete image received as

input, after that, within the face region we locate the

eye region. In the third step, we obtain the location

of the pupils’ centers. Then, we locate the corners

of each one of the eyes, and ﬁnally, we characterize

and classify the eye position using color information

about the sclera. Each one of these steps is going to

be discussed next.

2.1 Location of the ROI

Proper face location reduces the computational cost

of the next step of the methodology and make it less

error prone.

At this point, a frame of the video sequence is re-

ceived as input. To locate the face, due to the stabil-

ity of the domain and the certainty that the face will

always be in frontal position, the Viola-Jones object

detector (Viola and Jones, 2001) is applied, using an

optimized cascade for the detection of faces in frontal

position. A couple of samples of face detection can

be seen in Fig. 2.

Figure 2: Face detection samples.

Once the search area has been limited to the face

region, we need to locate now the eye region within

this area. At this step, the Viola-Jones object detec-

tor is applied again, but in this case, using a cascade

speciﬁcally trained for this domain. More than 1000

images of the eye area were manually selected for the

training, obtaining an accuracy of the 98% for the eye

region detection even when the eyes are closed. Fig.

3 shows the results of this step of the methodology.

Figure 3: Eye region detection sample.

2.2 Pupil Location

After the location of the eye region, this step is aimed

at the location of the pupil. The aim is to obtain the

location of the pupil’s center so we can use this loca-

tion as a reference point in the subsequent steps of the

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

314

methodology. To achieve this step a method based in

gradients (Timm and Barth, 2011) was applied. Yel-

low points in Fig. 4 show the results obtained with the

proposed method.

Figure 4: Yellow points represent the center of the pupil

obtained at this step.

2.3 Accurate Delimitation of Eyes

The fourth step of the methodology aims to locate the

eyes’ corners. Using as information the eye region

and the pupil location obtained in previous steps, we

designed a method consisting of three phases (see Fig.

5).

Figure 5: Phases of the delimitation of the eyes stage.

2.3.1 Phase 1: Selection of Candidate Points

First, we are going to detect points that can be con-

sidered as candidates to be the eyes’ corners. In or-

der to facilitate this detection, four areas of interest

are established using as reference the pupils’ centers.

For each eye, two search areas are deﬁned: one on the

right side and the other one on the left side. These two

areas correspond with those regions where the eyes’

corners are expected to be. So, within these four re-

gions, we are going to apply an interest operator. Par-

ticularly, we have applied the Shi-Tomasi method (Shi

and Tomasi, 1994). A sample of the results obtained

at this point can be observed in Fig. 6.

Figure 6: Interest operator applied over the four search ar-

eas.

As a result of this step, we obtain a set of points

that are candidates to be the eyes’ corners. Between

them, we need to choose those that better represent

the eyes’ corners.

2.3.2 Phase 2: Selection of Reference Points

Edge information is used at this step in order to obtain

the edges associated with the limits of the eyelids, so

they can be used as a reference of the eyes’ limits.

We use as input two areas of interest (one for each

eye) containing the eye. In order to facilitate edge de-

tection, we increase the enhancement of the eyelid by

increasing the contrast of the image. First, we convert

the images from the RGB color space to HSV color

space, in order to use the saturation channel S. It must

be considered that, regardless of the skin color, pix-

els from the sclera have always low intensity on the

saturation space due to their white color. Next, a ero-

sion ﬁlter considering the radius of the iris is applied.

The image obtained as a result from the application of

the erosion ﬁlter S

(x, y) is subtracted from the satura-

tion image S(x, y), obtaining this way the subtraction

image R(x, y) (as indicated in (1)). This process is

showed in Fig. 7, where it can be observed how the

low eyelid has now more contrast.

R(x, y) = S(x, y) −S

(x, y) (1)

Figure 7: Process of the enhancement of the eyelids’ con-

trast.

Next, a threshold for the binarization of the image

is computed using as reference some features of the

image according to (2), where µ is the average value

of the pixels from the difference image I

di f f

and σ is

the standard deviation.

= µ(I

di f f

) + 0.75 ∗ σ(I

di f f

) (2)

The binarization is computed according to (3),

where I

ths

is the thresholded image (see Fig. 8).

ths

(x, y) =







1 si I

di f f

(x, y) > th

0 otherwise

(3)

Figure 8: Thresholding the subtraction image R(x, y).

Although the eyelids are now easily segmentable,

there also exist other tiny elements that need to be

removed. These elements are small clusters of pixels

obtained from the thresholding. In order to remove

them, we are going to group the connected pixels as

blobs. Once all the pixels are grouped as blobs, we

take the biggest one and remove the remaining blobs.

This step is represented in Fig. 9, where the bigger

blob stays and the three tiny blobs are removed.

DetectionandCharacterizationoftheSclera-EvaluationofEyeGesturalReactionstoAuditoryStimuli

315

Figure 9: Blob ﬁltering for removing noise.

Next, considering the anthropometric constraints

that involve the human eye, we can deﬁne the eyes’

corners as the intersections between the ellipses that

represent the eyelid, which correspond with the lower

and upper limits over the x coordinate of the blob pre-

viously obtained. In the case of obtaining two points

with the same value for the x coordinate, we choose

the one that has a lower value for the y coordinate.

This way, the reference points obtained at the end of

this phase can be observed in Fig. 10.

Figure 10: Reference points for a sample image.

2.3.3 Phase 3: Choosing the Best Candidates

At this point, we are going to consider the candidate

points and the reference points obtained in the previ-

ous phases with the aim of choosing the best candi-

dates to correspond with the eyes’ corners.

The reference points obtained from the previous

phase are labeled as Pr

, Pr

and Pr

, where Pr

and Pr

represent the internal reference points (see

Fig. 11). We also consider the average size of the

eye tc

eye

and the inner distance dc

int

(where dc

int

the distance between Pr

and Pr

) computed for the

complete video sequence. According to (4) we accept

the reference point whenever the distance between the

ends of the eye is similar to the average size of the

eye tc

eye

computed for the complete video sequence.

right

represents the euclidean distance between Pr

and Pr

, d

le f t

is the euclidean distance between Pr

and Pr

and α is the allowed deviation. Otherwise,

we reject those reference points. The same occurs for

the inner reference points, where d

int

is the euclidean

distance between Pr

and Pr

, dc

int

the inner distance

computed for the video sequence and α is the allowed

deviation (see (5)).

f (d

le f t

, d

right

,tc

eye

, α) =



le f t

−tc

eye

| ≤ α

right

−tc

eye

| ≤ α

(4)

f (d

int

, dc

int

, α) =



int

− dc

int

| ≤ α , Accept

int

− dc

int

| > α , Reject

(5)

Figure 11: Location of the reference points.

Once the validity of the reference points has been

checked, with this information we compute the dis-

tances between the candidate points and the reference

points associated, ﬁnally choosing the candidate point

nearest to the reference point P

. In the case of two or

more candidate points with the same euclidean dis-

tance to the reference point, we compute the average

of those point according to (6), where P

represents

each one of the n candidate points with the same eu-

clidean distance to the reference point.

(x, y) = P



∑

i=1

∑

i=1



(6)

Finally, the quality of the selected points Pe

is an-

alyzed. If Pe

is far from the nearest reference point

, considering β as the maximum distance allowed,

that Pe

is going to be discarded and replaced by the

reference point Pr

, as indicated in 7.







si |Pe

− Pr

| < β

∀i ∈ {1 . . . 4}

otherwise

(7)

The results of this step of the methodology can be

observed in Fig. 12, where yellow points represent

the candidate points, red points correspond with the

reference points and green points represent the ﬁnal

points selected as eyes’ corners.

Figure 12: Choosing the best candidates: yellow for can-

didate points, red for reference points and green for the se-

lected eyes’ corners.

VISAPP2015-InternationalConferenceonComputerVisionTheoryandApplications

316

2.4 Eye Movement Characterization

The last step is the characterization and classiﬁcation

of the eye movement. This is going to be accom-

plished using information from the sclera, the white

area of the eyes. To that end, we need to estimate

the amount of white in the eye, using as reference the

characteristic points previously obtained. According

to the audiologist’s criteria, four eye movements are

considered as relevant in this domain: eye open, eye

closed, gaze shift to the left and gaze shift to the right.

First, the input image is converted to grayscale

and a histogram equalization is applied over it. For

the characterization of the movement, we are going

to compute a gray level distribution representing the

gray level for each one of the pixels located in the line

connecting both eyes’ corners. The result of this step

can be observed in Fig. 13.

Figure 13: Sample of the gray level distribution.

Once we have computed the gray level distribution

we divide it into our three areas of interest, i.e.: iris,

left side of the sclera and right side of the sclera. To

that end, we make use of the information provided by

the pupil’s center and the estimation of the radius of

the iris. This way, starting from the pupil’s center we

go through the gray level distribution, both to the right

and to the left, until we detect the ﬁrst white pixel that

indicates the boundary between the iris and the sclera.

This value is accepted whenever it does not exceed

the estimation of the radius of the iris. As a result of

this step, we obtain the delimitation of the three areas

of interest: iris, left side of the sclera and right side

of the sclera. The distribution of the delimitation of

these three areas can be observed in Fig. 14.

Figure 14: Delimitation of the three areas of interest over

the gray level distribution.

Next we discuss the rules for classifying the eye

status into the four categories established by the audi-

ologists.

• Eye Closed. Due to the absence of the sclera

when the eye is closed it is expected to have low

intensity of white values over the gray level distri-

bution. Considering this, we compute the summa-

tion of all the gray values G

for all the points in

the distribution. If the summation has a low value

we can consider that the eye is closed. Mathemat-

ically, this rule can be expressed as (8).

∑

i=1

< θ (8)

A sample of this category is represented in Fig. 15

where it can be observed that there is no white in-

formation along the distribution, which represents

that the eye is closed.

Figure 15: Gray level distribution for closed eye.

• Eye Open. It is the opposite case of the previ-

ous state eye closed, so, in this case, we are going

to have white information associated to the sclera.

This classiﬁcation allows a subsequent classiﬁca-

tion between gaze to the left and gaze to the right.

The mathematical representation is the opposite

of the previous status (9), where G

represents the

gray value of each one of the n points, and θ is the

same threshold. Fig. 16 shows an image sample

where it can be observed from the gray level dis-

tribution that there is white information from the

sclera.

∑

i=1

≥ θ (9)

Figure 16: Gray level distribution for open eye.

• Gaze Shift to the Right. This status is only pos-

sible when the eye is open. In order to distin-

guish it we are going to use the information previ-

ously obtained about the delimitation of the areas

DetectionandCharacterizationoftheSclera-EvaluationofEyeGesturalReactionstoAuditoryStimuli

317

of interest, in this case: left side of the sclera and

right side of the sclera. When the eye is classiﬁed

as open, we compute the summation of the gray

level values for each one of the sides of the sclera.

Next, it is checked whether the summation of the

right side of the sclera represents a small part of

the total summation of both sides. This can be ex-

pressed mathematically as in (10) where Ed rep-

resents the n

points located in the right side of the

sclera, T

represents the n points of both sides of

the sclera and β is a threshold empirically estab-

lished with value 0.20. A sample of this status is

showed in Fig. 17.

∑

i=1

≤ β ∗

∑

j=1

(10)

Figure 17: Gray level distribution for gaze shift to the right.

• Gaze Ghift to the Left. This status is deﬁned

analogously to the previous status. Is this case, it

is checked whether the summation of the left side

of the sclera represents a small part of the total

summation of both sides. In (11), Ei represents

the n

points located in the left side of the sclera,

the n points of both sides of the sclera and β

is the threshold. Fig. 18 shows a sample of this

status.

∑

i=1

≤ β ∗

∑

j=1

(11)

Figure 18: Gray level distribution for gaze shift to the left.

3 EXPERIMENTAL RESULTS

Given the preliminary nature of this study, the aim

was to test the viability of the methodology over a

small dataset. Besides, due to the difﬁculties associ-

ated with the obtaining of permits for recording peo-

ple with severe cognitive decline (permits signed by

them may not have legal validity in severe cases, so

family permits would be needed), in this initial ap-

proach three different volunteers were instructed in

order to reproduce the typical movements of our tar-

get patients.

The proposed methodology is applied frame by

frame over three video sequences recorded during the

performance of the audiometry. The image acquisi-

tion is quite simple, the video camera must be located

behind the audiologist (the audiologist is seated in

front of the patient) and the recorded scene must show

the patient’s face and the audiometer (a sample of this

scenario can be observed in Fig. 2) Video sequences

were recorded in high resolution (1080x1920 pixels)

with a frame rate of 25 FPS (frames per second). Each

video sequence corresponds with a different patient

and they have an average duration of 6 minutes. So,

with a frame rate of 25 FPS and an average duration

of 6 minutes, we analyze an average of 9000 frames

for each video sequence.

The θ threshold presented in Section 2.5 was em-

pirically established as θ =

1700

where n is the aver-

age size of the eye.

This experiment is divided into two studies: the

analysis of accuracy in the classiﬁcation of eye move-

ments and the analysis about the detection of eye ges-

tural reactions to the sound.

3.1 Movement Classiﬁcation Accuracy

The aim here is to study the suitability of the method

in the classiﬁcation of the eye movements. Three

video sequences from three different hearing assess-

ments were analyzed and classiﬁed frame by frame.

Table 1 shows the accuracy for each one of the four

eye movement categories considered in this domain

(it must be noted that the category Eyes open not only

correspond to the situation in which the eyes are open

with the gaze ﬁxed to a central point, but it also con-

tains the categories Gaze shift to left and Gaze shift to

right). The results are quite acceptable since they are

above 82.84%. The high accuracy obtained for the

category Eyes open is justiﬁed because the empirical

threshold used here is optimized for this class since

it contains the gaze movements, that are the relevant

categories in this domain. It is important to emphasize

that the main goal of this work is not the correct clas-

siﬁcation of the eye movements, but to detect the eye

gestural reactions to the stimuli, which is the analysis

that we are going to conduct next.

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318

Table 1: Accuracy for each one of the eye movement cate-

gories.

Eyes Eyes Gaze shift Gaze shift

closed open to left to right

Accuracy 84.31% 98.2% 85.89% 82.84%

3.2 Detection of Reactions to the Sound

As commented before, the great contribution that we

can provide to the audiologist is a proper detection of

the eye movements associated with reactions to the

auditory stimuli. Since the video sequences have a

frame rate of 25 FPS we know for certain that a re-

action will last more than one frame, this is why we

are not concerned about obtaining a high success rate

in classiﬁcation, because if a typical reaction lasts be-

tween 10 and 15 frames, the misclassiﬁcation of one

frame will not affect the proper detection of the reac-

tion.

For this experiment, we consider that a status can

be established when three or more consecutive frames

receive the same category in classiﬁcation. Results

are detailed in Table 2, where we evaluate the agree-

ment between the methodology and the audiologists

based on the number of reactions to the stimuli de-

tected by each one of them. The agreement between

the methodology and the audiologists is complete

(100% of agreement) for the video sequences eval-

uated in this test.

Table 2: Evaluation in the detection of reactions to the

sound. Results are expressed in number of reactions.

Gaze shift Gaze shift

to left to right

Expected Detected Expected Detected

Video 1 17 17 15 15

Video 2 17 17 21 21

Video 3 20 20 18 18

Agreement 100% 100%

4 CONCLUSIONS

This work proposes a novel methodology for the de-

tection and identiﬁcation of eye gestural reactions to

the auditory stimuli in order to facilitate the hearing

assessment of patients when no cooperation exists.

This task is accomplished using information about

the color distribution of the sclera. The results ob-

tained in this ﬁrst approach point out the suitability

of the method for the detection of these speciﬁc kind

of reactions. Besides, we want to highlight the fact

that is methodology is an initial proposal where we

have classiﬁed the eye movements into the four cate-

gories established by the audiologists, but it is impor-

tant to note that the methodology is generic proposal

that would allow us to model or to characterize other

eye movements.

Several experiments were conducted in order to

obtain the ﬁnal methodology. Four of them were ori-

ented to choose the best method for different steps of

the methodology: study of three different techniques

for pupil detection (Section 2.3), survey about differ-

ent interest operators for the selection of candidate

points (Section 2.4.1), analysis about the suitability

of the reference points (Section 2.4.2) and analysis of

the suitability of the candidate points chosen as eyes’

corners (Section 2.4.3). However, in order to sum-

marize, we only detail here a ﬁnal experiment which

analyzes the behavior of the ﬁnal methodology.

It must be highlighted that the proposed methodol-

ogy is a noninvasive method. In contrast to other eye

tracking methods, this solution that not require special

devices, or that the patients wear special glasses or

other contact elements neither a change in the typical

procedure of the audiologists. This is highly impor-

tant, since the need of an scenario as natural as possi-

ble has been highlighted by the audiologist in order to

not alter as much as we can the environment in order

to not to inﬂuence the natural responses of the patient.

Despite of the promising results, there still exist

some points that will be attempt as future works. First

of all, we need to obtain more video sequences in or-

der to increase our dataset and, thus, extend the ex-

perimental results with a more complete survey. This

is a complicated step, since it is not easy to obtain the

necessary permissions to record this special patients.

We can also consider the integration of this methodol-

ogy with the information provided by the audiometer.

The methodology could also be considered in other

domains such as the diagnosis of nystagmus, a condi-

tion of involuntary eye movement that may result in

reduced or limited vision.

The ﬁnal contribution of this work might be very

interesting for the audiologist community since it is

a novel method for the detection of eye based ges-

tural reactions. This methodology will facilitate the

hearing assessment of patients with sever cognitive

decline or other communication difﬁculties, patients

that can not be evaluated following a standard proce-

dure. A proper hearing assessment of these patients

is more difﬁcult to conduct, but it is very important to

solve this issue since a proper evaluation may help to

treat the hearing loss and improve the quality of life

of these patients.

DetectionandCharacterizationoftheSclera-EvaluationofEyeGesturalReactionstoAuditoryStimuli

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ACKNOWLEDGEMENTS

This research has been economically supported in part

by the Consellera de Cultura, Educacin e Ordenacin

Universitaria of the Xunta de Galicia through the

agreement for the Singular Research Center CITIC

also by the Secretara de Estado de Investigacin of

the Spanish Government through the research project

TIN2011-25476.

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