Development of a Computer Interface for People with Disabilities based

on Computer Vision

Gustavo Scalabrini Sampaio and Maur

ıcio Marengoni

Postgraduate Program in Electrical Engineering and Computing, Universidade Presbiteriana Mackenzie, S

ao Paulo, Brazil

Keywords:

Human-computer Interfaces, Computer Vision, Face Tracking, Face Landmarks, People with Disabilities.

Abstract:

The growing of the population with disabilities in the world must be accompanied by the growing of research

and development of tools that help these users on basic computer activities. This paper presents the develop-

ment of a system that allows the use of personal computers using only face movements. The system can be

used by people with motor disabilities who still have head movements, such as superior members amputees

and tetraplegic. For the development of the proposed system, the most efﬁcient techniques in previous works

were collected and analyzed, and new ones were developed in order to build a system with high performance

and precision, ensuring the digital and social inclusion of the target public. Tests have shown that the tool is

easy to learn, has a good performance and can be used in everyday computer applications.

1 INTRODUCTION

Computing and communication, from the point of

view of resources and usage, are constantly growing,

offering to the users tools for learning, working, en-

tertaining, getting information and socializing. This

development have been accompanied by new ways for

user interaction, such as touchscreens, voice recogni-

tion, virtual reality glasses, among others. Even with

these technological advances, which have given, for

the majority of users, more convenience and easier

interfaces, people with disabilities still have difﬁculty

to interact with computers.

The World Health Organization (WHO, 2011)

points out that about 1 billion people in the world

have some kind of disability and describes the bar-

riers these people face, one of which is the access to

information and communication. Devices like smart-

phones and computers, despite having some accessi-

bility resources, are not developed taking into account

the needs of people with disabilities, often making

it impossible for these people to use these devices.

For the WHO, the computer technologies developers

and researchers should strive to provide for users with

disabilities the same experience that other users have,

thus ensuring their social inclusion and academic and

professional development.

This work was developed taking into account the

needs of users with disabilities, the WHO suggestion

for development computer interfaces based on face

movement and to ensure, through the access of the

media, the social and digital inclusion of this public.

Will be presented the development of a natural com-

putational interface based on computer vision, that al-

lows access to the resources of the Windows operat-

ing system by the user through face movements, eyes

blinking and mouth opening and closing. No move-

ment of the lower parts are required, so the tool can

be used for people with numerous degrees of motor

disabilities, especially users who have low precision

or amputation of the upper limbs up to tetraplegics.

The proposed system was developed using concepts

and techniques that proved to be efﬁcient in similar

systems, and presents high processing performance,

contributing to a better user experience. In addition to

performance, this paper presents other contributions:

• The mouse cursor movement occurs smoothly and

accurately.

• Unlike other systems, allows a simulation of typ-

ing by face movements.

• Allows the user to conﬁgure resources, such as

the system’s operating mode, the keystroke or the

click that will be activated, enable or not the func-

tions related to the eyes and mouth and to adjust

the sensitivity of the cursor movement.

• Has a control mechanism, where the user can

start, stop or pause its operation.

• Implements auxiliary algorithms to improve the

efﬁciency of facial detection and perform a system

user search automatically.

566

Sampaio, G. and Marengoni, M.

Development of a Computer Interface for People with Disabilities based on Computer Vision.

DOI: 10.5220/0006616205660573

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

566-573

ISBN: 978-989-758-290-5

2 RELATED WORKS

The analysis of similar works to the proposed is im-

portant to determine the state of the art and to ﬁnd

the main characteristics of the system in use today.

In a constructive way, the positives and negatives as-

pects of each work were identiﬁed. In general, sys-

tems with interfaces using face movements are basi-

cally composed of a face detection module, a module

for converting face movement to mouse cursor posi-

tioning, and a module for click simulation or other

functions for computer interface. Among the works

analyzed, the works presented by (Betke et al., 2002)

and (Pallej

a et al., 2011) stand out, for making the

tool available for download and using techniques that

make it possible to use in computers with a webcam.

The analysis of other works was help to deﬁne the

functionalities of the proposed system and the most

efﬁcient techniques to execute some function. The

works of (Tu et al., 2005), (Fu and Huang, 2007),

(Xu et al., 2009), (Pallej

a et al., 2011) and (Kraichan

and Pumrin, 2014) use for face detection the tech-

nique proposed by (Viola and Jones, 2001) that has

as training method the Adaboost and Harr Cascade as

detection method. (Ji et al., 2014) and (Gor and Bhatt,

2015) indicate that this technique, coupled with the

search area reduction through the skin color segmen-

tation, improves detection performance and accuracy.

The technique presented by (Xu et al., 2009) that uses

Active Appearance Models (AAM) to map the points

on the face and use them to move the mouse cursor is

a simple way to accomplish this task. Apply a transfer

function to perform the mapping between face posi-

tion and cursor position as shown by (Kjeldsen, 2006)

and (Pallej

a et al., 2011) is important to ensure accu-

racy and smoothness of cursor movement.

The performance in this type of system is essen-

tial. Delays in processing reduce the mouse cursor

positioning precision and the efﬁciency of state def-

inition of eyes and mouth. (Kraichan and Pumrin,

2014) shows that the face detection module can have

its performance increased representatively by not per-

forming face detection in a range of frames, where

the system uses the face position found in the last de-

tection to perform its analysis. This technique works

because the user, in a range of 3 to 5 frames, consid-

ering a system with performance of at least 30 frames

per second (fps), can not perform movements with

great displacement. These techniques were used for

the development of the proposed system and new ones

were added, in order to produce a tool that uses only

the computer webcam and has an acceptable perfor-

mance.

3 SYSTEM DEVELOPMENT

The proposed system was designed to perform its

functions using a webcam and uses, in addition to

face movement, the opening and closing of eyes and

mouth to control the resources of the Windows oper-

ating system. The system does not require calibration

and it has 2 operation modes:

• The mouse mode allows the user to perform the

mouse functions with the face movement. The

mouse cursor is controlled by the direction of the

user’s nose, this reference point gives the user pre-

cision to select icons and buttons of at least 30x30

px. The mouth opening and closing simulates the

clicks with the mouse’s left button, and the blink

of the right and left eyes allow the simulation of

functions chosen by the user. This mode has a

drag function, when the mouth is opened for about

1s an object can be dragged, when the user close

his mouth the object is released. This mode allow

the user, together with a virtual keyboard, access

the internet, social networking and learning tools.

• The keyboard mode, not present in other works,

allows the user to simulate keyboard typing using

face movements and the eyes and mouth open-

ing and closing, face movement can simulate up

to 4 keystrokes, activated by moving the face up,

down, left and right, the user can conﬁgure which

keyboard key will be simulated on each move.

This mode mode allows the user to play simple

games and use tools developed to people with dis-

abilities based on selectable symbols.

The programming language used for the develop-

ment of the proposed system was C++, with OpenCV

and Dlib libraries. The techniques used to implement

the system’s functionalities will be detailed below.

3.1 Pre-processing

In order to reduce the search area and increase accu-

racy in face detection, some pre-processing has been

implemented. These pre-processing aims to ﬁnd the

image region that has skin color, that region is ex-

tracted and passed to the face detector. The ﬁgure 1

shows the preprocesses executed step by step.

The ﬁrst procedure performed is the image bright-

ness adjustment, necessary to improve the efﬁciency

of the skin color segmentation. The color domain

is converted from RGB (a) to YCrCb (b). In the

YCrCb domain the Y channel represents the bright-

ness and the Cr and Cb channels together represent

the colors. The brightness adjustment is performed

on the Y channel using histogram equalization tech-

nique (c). After brightness adjustment, the skin color

Development of a Computer Interface for People with Disabilities based on Computer Vision

567

Figure 1: Pre-processing for the face detection function of the proposed system. a) Input frame; b) RGB color domain

conversion to YCrCb; c) Brightness adjustment by histogram equalization; d) Skin color segmentation; e) Noise ﬁlter; f)

Final image where face detection is performed.

is segmented by ﬁltering the pixels based on intervals

of YCrCb channels values (d), the range of values of

the Cr and Cb channels is small, varying further in the

Y channel. After skin color segmentation, the result-

ing mask presents noise that, in practice, are sparse

pixels having color in the interval deﬁned by the seg-

mentation ﬁlter, this noise is removed applying a me-

dian blur ﬁlter in the image (e). Finally the initial

search region for face detection is deﬁned by a rect-

angle of dimensions delimited by the pixels ﬁltered of

the mask (f). It is observed the signiﬁcant reduction

of the search area and the consequent improvement in

face detection performance.

3.2 Face Detection

The face detection is the main element of the pro-

posed system, and it is strongly related to system per-

formance. The search for the user’s face is performed

with the help of the OpenCV library and it happens in

2 different search modes.

The ﬁrst search mode is performed when no face

has been found yet, the system then performs all the

pre-processing in each frame deﬁning the region that

should be used by the face detector, the process is re-

peated until a face is found. Once one or more faces

are detected its positions represented by rectangles are

stored in a vector, then the system ﬁlters the face clos-

est to the center of the image and assumes this face as

the user of the system. The detected face position re-

ceives a 13% additional dimension offset, this value

was determined empirically considering processing

speed and detection accuracy. Figure 2 presents the

face detection in the region deﬁned by skin color seg-

Figure 2: Face detection performed in the region deﬁned by

skin color segmentation, the region detected is augmented

by an offset of 13%.

mentation. The second face search mode is performed

every 3 frames, in the area deﬁned by the rectangle

show in ﬁgure 2, which allows small user movements

among frames. If a face is found the system continue,

otherwise it returns to the ﬁrst face search mode. The

second face search mode signiﬁcantly increases the

system’s performance. It was veriﬁed that this tech-

nique practically double the system performance.

3.3 Face Landmark Detection

Having the face location is not enough for the system

to execute the control of the operating system, so it

is necessary to look for more elements that allow the

system to identify the face’s position and other face

elements such as eyes, mouth and nose.

The proposed system use face landmarks to esti-

mate the face position. These points provides also in-

formation about the position of the eyes and mouth.

Unlike the optical ﬂow technique, which needs to

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

568

Figure 3: Position of the 68 face landmarks detected.

Source: (Sagonas et al., 2013).

perform the analysis using information of 2 or more

frames, the use of the face landmarks allows estimat-

ing the face position only with the information of the

last frame. The face position is used to control the

mouse cursor and simulate typing.

The implementation of this feature was performed

with help of Dlib library, that provides an algorithm

for face landmark detection based on the algorithm

described by (Kazemi and Sullivan, 2014), that uses

regression trees to estimate the position of face land-

marks. This algorithm runs in the area of face de-

tected and ﬁnds 68 face landmarks, proposed by (Sag-

onas et al., 2013), as shown in ﬁgure 3.

3.4 Mouse Cursor Positioning

After all detection operations, the system has the nec-

essary data to transform the face position into the

mouse cursor position. This task is performed on all

frames and use the face landmarks, the points 40 and

43 in ﬁgure 3 are used to deﬁne the x axis. Points

1 and 4 are used as limits for the y axis. Point 31,

located at the tip of the user’s nose, was chosen as

reference point to control the mouse cursor because

its movement is natural to the user.

The operation of converting the face position to

mouse cursor position has 4 steps. The ﬁrst is to ﬁnd

the percentage value of the reference point position in

relation to the limits of the axis, this percentage is cal-

culated considering a dead zone of 20% at each end

of the limit, necessary to avoid values higher or lower

than the established limits, a situation that can happen

due to the constant detection of the face landmarks.

The second is to convert this percentage to the corre-

sponding pixel value of the captured frame, by deﬁn-

ing the reference cursor position. This direct con-

version generates an unstable positioning, since small

displacements on the reference point cause large ref-

Figure 4: Reference points and regions used in the proposed

system.

erence cursor displacements in the frame. The imple-

mentation of the third step was necessary to eliminate

the instability of positioning and to ensure precision

and smoothness in the cursor movement. In this step

the system analyzes the previous and current refer-

ence cursor position, this last is the position desired

by the user, and it calculates its new position through

of a transfer function. The transfer function was de-

veloped based on the behavior of the natural logarith-

mic curve, where the values resulting from the func-

tion ln(x) are used as percentage of movement in rela-

tion to the reference cursor position and the position

desired by the user. The last step is to perform the

position conversion of reference cursor, which has a

scale equal to the frame resolution, to the position in

the scale of the user’s monitor. With this last calcu-

lated position, a command is issued to the operating

system to position the mouse cursor.

The steps shown are used in mouse mode, for key-

board mode operations the execution occurs until step

2, that position is then interpreted as the user face di-

rection. For each face direction a keyboard key typ-

ing is simulated by the operating system. The ﬁgure

4 illustrates the points and regions used to interpret

face movement. The green dots indicate the face land-

marks detected. The blue dots represent the reference

limits of the axes. The red dot represents the reference

point for moving the mouse cursor. The orange dots

represent the axis reference position converted to the

position in the captured frame. The yellow dot shows

the reference mouse cursor. The white rectangles in-

dicate the face and eyes detected. The cyan rectangle

represents the reference region of the user screen. The

magenta rectangle represents the limits for setting the

face position in keyboard mode.

Development of a Computer Interface for People with Disabilities based on Computer Vision

569

Figure 5: Proposed system ﬂowchart.

3.5 Clicks and Typing

The clicks and typing simulation are performed

through the face movements, in keyboard mode, and

by the interpretation of the eye and mouth move-

ments. The user can choose the function that each

movement perform, these function may be a click or

a typing simulation. In mouse mode the mouth is only

used to perform the mouse left button click. For the

system to activate a certain function, it is ﬁrst neces-

sary to deﬁne the state of the mouth and eyes.

The mouth state deﬁnition is performed using

points 22 and 9, in ﬁgure 3, as y axis reference and

points 63 and 67, the latter being located at the top

and bottom of the lips and varying in distance during

opening and closing of the mouth. The ”open” state

is detected if the distance between points 63 and 67

is greater than 3% of the distance between points 22

and 9. This value allows the user to remain with the

mouth slightly open to breathe without activating the

click, a small opening movement perform the action.

A drag function was implemented in the system, to do

this the user must remains with the mouth open for 50

frames, which corresponds to 0.5s to 1s, when closing

the mouth the object is released.

The deﬁnition of eye states could not be per-

formed using the face landmarks directly, since the

camera’s low resolution prevents the points detected

in the eye region to vary the distance between them

satisfactorily. These points were only used to deter-

mine the eyes region and extract that region for anal-

ysis. The eye state deﬁnition is simply the detection

of the eye, using the same technique used for the face

detection proposed by (Viola and Jones, 2001), but

with a trained base with positive images of open eyes.

If the eye is detected its state is ”open”. To increase

the system performance, this operation is performed

only every 5 frames. The ﬂowchart in ﬁgure 5 illus-

trates the operations performed by the system.

4 TESTS AND RESULTS

To demonstrate that the tool is easy to use, learn and

has a good performance, 4 types of tests were per-

formed with 10 users without any type of disability.

Each test encourages the use of certain functionality

or simulates an environment of use, allowing to eval-

uate if the tool meets the need of the user to perform

computer tasks. In terms of performance, the pro-

posed tool performs its operations by processing 100

fps. Value 3 times greater than the Camera Mouse

proposed by (Betke et al., 2002) and Head Mouse pro-

posed by (Pallej

a et al., 2011).

The system was conﬁgured equally to all users,

and the testing room have no special illumination.

The lights were artiﬁcial and placed at the ceiling of

the room. The machine has a Intel Core processor i3-

3110M dualcore of 2.40Ghz, 8GB memory, Windows

10 of 64 bits and a 640x480 pixels webcam.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

570

4.1 Test 1 - Mouse Functions

(Bian et al., 2016) presents a standardized form, de-

ﬁned by ISO/TS 9241-411 standard, to evaluate the

performance of the movement and clicks of a pointing

interface. (Soukoreff and MacKenzie, 2004) presents

more details about this kind of test and points out

several recommendations. This standard deﬁnes the

test environment and the calculations of values of the

measured parameters. The test environment is com-

posed of circular regions organized in a circle shape.

The parameters are measured according to the dis-

tance values between the targets (D) and the size of

the target (W), the ﬁgure 6 shows an example of a

test environment, the target is presented to the user

in green color. The tool performance is measured ac-

cording to its throughput (TP) deﬁned by

T P =

(1)

representing the amount of information that the

user can transmit to the operating system using the

tool in bits/s. ID represents the difﬁculty index of the

environment and is deﬁned by

ID = log

(

+ 1), (2)

MT is the average time that the user moves the

mouse cursor from one target to another. (Souko-

reff and MacKenzie, 2004) draws attention to this

measure of time, since the standard disregards the

target selection time, which in practice could gener-

ate measurements based only on velocity, eliminat-

ing the precision factor. According to (Soukoreff and

MacKenzie, 2004) measuring only velocity with ab-

sence of precision becomes a non-informative mea-

sure and recommends that collected values that do not

consider precision should be taken from the analysis.

To ensure the credibility of the tests performed, the

test environments used to evaluate the proposed tool

consider the time of movement and selection.

Table 1: Environments features of movement and click

tests.

Environment D W ID

1 534 76 3.00

2 534 57 3.37

3 305 57 2.67

The users were submitted to 3 different environ-

ments, for each environment 3 test rounds were ex-

ecuted. Users performed the tests without ever hav-

ing used the tool, which made it possible to ana-

lyze the usage learning curve and to verify if the tool

can be used intuitively, only by explaining to users

Figure 6: Test environment of movements and clicks with

indication of measures.

Figure 7: Path traveled by mouse cursor and click points,

test performed in test environment 1.

that the tasks should be performed with face move-

ments. Table 1 presents the characteristics of the 3

test environments. The table 2 was generated with

the mean of the measured values for each environ-

ment, where TCT represents the task completion time

in seconds, MT represents the mean time of move-

ment and selection from one target to another in sec-

onds and TP represents the throughput in bits/s (the

larger the values the better the throughput). The Final

Average showing the ﬁnal result of the test. In this

table is possible to verify that once the user learns

how to use the system the time for task completion

reduces, even with the increase of the difﬁculty index

among the environments. For comparison the mouse

throughput is approximately 4.50 bits/s. The ﬁgure

7 shows the path traveled by the mouse cursor and

the click points of one of the tests. It can be veri-

ﬁed that there are no noisy paths and few adjustments

in the region of the targets. After some interactions

with the tool the user demonstrates ability using the

tool, facilitating the accomplishment of the tasks af-

ter each repetition. The table 3, together with the ﬁg-

ure 8, demonstrate the task completion time reduction

and increased throughput of the test, ”1-2” and ”2-

3” columns show the decrease and growth between

rounds 1 and 2, and 2 and 3, respectively.

This test shows that the proposed tool is easy to

use, has a high precision and it allows the user to

move the mouse cursor smoothly.

Development of a Computer Interface for People with Disabilities based on Computer Vision

571

Table 2: Average results of the rounds of each test environment.

Rounds

1 2 3

Environment TCT MT TP TCT MT TP TCT MT TP MTCT MMT MTP

1 67.48 4.50 0.82 43.68 2.91 1.07 40.82 2.72 1.16 50.66 3.38 1.02

2 47.00 3.13 1.14 46.24 3.08 1.16 43.25 2.88 1.25 45.50 3.03 1.18

3 40.10 2.68 1.06 40.34 2.69 1.06 36.34 2.42 1.20 39.11 2.61 1.10

Final Average 45.09 3.01 1.10

Table 3: TCT Reduction and TP growth between the test

rounds.

TCT Reduction TP growth

1-2 2-3 1-2 2-3

35 % 7 % 31 % 8 %

2 % 6 % 1 % 8 %

-1 % 10 % -1 % 14 %

Figure 8: Increased throughput between test rounds.

4.2 Test 2 - Virtual Keyboard Writing

The second test was a simulation of a common task in

personal computers: typing a text. With this feature it

is possible to conduct searches on the internet and use

social sites, it is also possible to access training and

distance learning sites as well as professional tools.

Three sentences were written using a virtual key-

board, the keys on the keyboard measure 30x30 px.

The sentences have different sizes to allow the anal-

ysis of learning the task and the use of the tool in a

small and restricted area. The phrases elaborated were

”Bom dia” (Good morning), ”Vis

ao Computacional”

(Computer vision) and ”Vamos assistir ao jogo” (Let’s

watch the game). The user was instructed not to write

special characters and space, if an error occurred it

was not necessary to delete the wrong letter. The writ-

ing time averages were 30s for sentence 1, 83s for

sentence 2 and 81s for sentence 3, so writing sentence

3, the longest, took a time close to sentence 2, this

means that the user has learned the task and can exe-

cute it faster every attempt.

4.3 Test 3 - Click Evaluation

The third test had more technical goals, for evaluating

the effectiveness of clicks with the eyes and mouth. A

test environment was built with a large 200x200 px

button positioned in the center of the screen, the test

system itself instructed the user to click that button

using the opening and closing of the eyes and mouth.

For each eye and for the mouth it was counted how

many clicks were possible in 20s. On average, users

were able to click 48 times with their mouth, 13 times

with their right eye and 11 times with their left eye.

The efﬁciency of the clicks with the mouth is much

higher then clicks performed with the eyes, this dif-

ference comes from the technique used to determine

the state of the mouth and eyes.

4.4 Test 4 - Face Typing Evaluation

The fourth test was carried out to evaluate the efﬁ-

ciency of keyboard typing. The test environment pre-

senting 4 squares with size 200x200 px in the cen-

ter of the screen forming a cross. These squares al-

ternated between the green and gray colors, when a

square is green the user had to move the face in the

direction of the square in order to ”type it”, system-

ically the user activated the keys A, S, D and W. A

sequence of 20 movements was elaborated, each di-

rection was requested 5 times. The task of typing can

be used in simple games and in request tables, where

an icon in a position describes an item (such as drink,

fruits, etc) desired by the user. On average, users were

able to perform the task in 23s. Most users did the test

at a frequency below 1 typing per second, enabling the

use of this functionality in real applications.

5 CONCLUSION

The system proposed used efﬁcient techniques for

face detection, interpretation of face movements, con-

version of face position to mouse cursor position and

other techniques to allow users to interact with per-

sonal computers in a simple and intuitive way. The

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

572

system using only a webcam and delivering a per-

formance of 100 fps. Face detection has high per-

formance, from the implementation of auxiliary tech-

niques, such as skin color segmentation and detection

cut between frames. Face landmarks allowed to iden-

tify the face movement, this signal is converted by

a transfer function to a accurate and smooth mouse

cursor movement. The system also enables the face

movement to perform the typing simulation. The

opening and closing of eyes and mouths have been

translated for simulation of clicks and typing.

From the tests performed, it was demonstrated the

efﬁciency of the tool and the ease of users to learn

how to interact with the system and perform simple

computer tasks. People with disabilities of the up-

per limbs and spinal cord injury, as long as they have

the head movement, can use this tool and enjoy the

resources available in the computer and the internet.

The digital inclusion of these people can stimulate the

increase of their self-esteem and provide opportuni-

ties for academic and professional development.

As future works, the proposed system must un-

dergo more tests of comparison with other existing

similar tools. Tests should also be performed with

users with disabilities in order to conﬁrm the usabil-

ity of the system. After these tests and possible ad-

justments in its functionalities, the tool must be made

available to the public.

Human-computer interfaces aimed to people with

disabilities should always be research and develop-

ment topic, as society must ensure that these users are

included in all activities. Future systems may deter-

mine more efﬁcient techniques of using the opening

and closing of the eyes as an interface to the system,

considering a low-resolution and reduced-size input

image without performance decrease. These systems

should always provide the best performance possible,

as low performance makes the system unusable in real

situations or affect their usability.

REFERENCES

Betke, M., Gips, J., and Fleming, P. (2002). The camera

mouse: Visual tracking of body features to provide

computer access for people with severe disabilities.

IEEE Transactions on Neural Systems and Rehabili-

tation Engineering, 10(1):1–10.

Bian, Z.-P., Hou, J., Chau, L.-P., and Magnenat-Thalmann,

N. (2016). Facial position and expression-based hu-

man–computer interface for persons with tetraplegia.

IEEE Journal of Biomedical and Health Informatics,

20(3):915–924.

Dlib (2017). Home page. Available in: http://dlib.net/. Ac-

cess on May 31, 2017.

Fu, Y. and Huang, T. (2007). hMouse: Head tracking driven

virtual computer mouse. In 2007 IEEE Workshop on

Applications of Computer Vision. IEEE.

Gor, A. K. and Bhatt, M. S. (2015). Fast scale invari-

ant multi-view face detection from color images us-

ing skin color segmentation & trained cascaded face

detectors. In 2015 International Conference on Ad-

vances in Computer Engineering and Applications.

IEEE.

Ji, S., Lu, X., and Xu, Q. (2014). A fast face detection

method combining skin color feature and AdaBoost.

In 2014 International Conference on Multisensor Fu-

sion and Information Integration for Intelligent Sys-

tems (MFI). IEEE.

Kazemi, V. and Sullivan, J. (2014). One millisecond face

alignment with an ensemble of regression trees. In

Proceedings of the IEEE Conference on Computer Vi-

sion and Pattern Recognition, pages 1867–1874.

Kjeldsen, R. (2006). Improvements in vision-based pointer

control. In Proceedings of the 8th international ACM

SIGACCESS conference on Computers and accessi-

bility - Assets 06. ACM Press.

Kraichan, C. and Pumrin, S. (2014). Face and eye track-

ing for controlling computer functions. In 2014

11th International Conference on Electrical Engi-

neering/Electronics, Computer, Telecommunications

and Information Technology (ECTI-CON). IEEE.

OpenCV (2017). Home page. Available in:

http://opencv.org/. Access on May 31, 2017.

Pallej

a, T., Guillamet, A., Tresanchez, M., Teixid

o, M., del

Viso, A. F., Rebate, C., and Palac

ın, J. (2011). Im-

plementation of a robust absolute virtual head mouse

combining face detection, template matching and op-

tical ﬂow algorithms. Telecommunication Systems,

52(3):1479–1489.

Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., and Pantic,

M. (2013). 300 faces in-the-wild challenge: The ﬁrst

facial landmark localization challenge. In 2013 IEEE

International Conference on Computer Vision Work-

shops. IEEE.

Soukoreff, R. W. and MacKenzie, I. S. (2004). Towards a

standard for pointing device evaluation, perspectives

on 27 years of ﬁtts’ law research in hci. International

Journal of Human-Computer Studies, 61(6):751–789.

Tu, J., Huang, T., and Tao, H. (2005). Face as mouse

through visual face tracking. In The 2nd Canadian

Conference on Computer and Robot Vision (CRV 05).

IEEE.

Viola, P. and Jones, M. (2001). Rapid object detection us-

ing a boosted cascade of simple features. In Proceed-

ings of the 2001 IEEE Computer Society Conference

on Computer Vision and Pattern Recognition. CVPR

2001. IEEE Comput. Soc.

WHO (2011). World Report on Disability. World Health

Organization.

Xu, G., Wang, Y., and Feng, X. (2009). A robust low cost

virtual mouse based on face tracking. In 2009 Chinese

Conference on Pattern Recognition. IEEE.

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