Seminar report on blue eyes

SEMINAR REPORT
on
BLUE EYES TECHNOLOGY
Submitted in partial fulfillment of requirements for the award of
Bachelor of Technology
in
Computer Science and Engineering
Submitted by:
Ms. Roshmi Sarmah
Student ID: ET13BT0306
School of Engineering and Technology
Department of Computer Science and Engineering
The Assam Kaziranga University
October 2015

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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEEING
SCHOOL OF ENGINEERING AND TECHNOLOGY
THE ASSAM KAZIRANGA UNIVERSITY
JORHAT-785006 :: ASSAM :: INDIA
CERTIFICATE
This is to certify that the seminar entitled “Blue Eyes Technology” has been
successfully presented by Ms. Roshmi Sarmah bearing ID ET13BT0306 of
5th semester, B.Tech (Computer Science and Engineering) programme under
The Assam Kaziranga University. The conceptualization and presentation of
this seminar report was carried out by her under the mentorship of Ms. Ankita
Goyal.
This report is recommended by the Department of Computer Science and
Engineering for the partial fulfillment of the degree Bachelor of Technology.
………………………….. ………….…………………..
HOD, Dept. of CSE Faculty Mentor, Dept. of CSE

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ABSTRACT
The whole idea behind the Blue Eyes jargon is to explore the possibility of creating a
computer, which can interact with us just like the way we human beings interact with each
other. For example, we can imagine of a fine morning when we walk on to our computer
room and switch on the computer, and the device actually can interpret our gloomy mood. As
a response, it might also open our mail box and show us some of the mails we like and try to
cheer us up. It seems to be a fiction today, but soon it will be the life lead by BLUE EYES
TECHNOLOGY in the very near future.
The basic idea behind this technology is to give the computer the human power and
cognition. We all have some perceptual abilities. That is why we can understand each other’s
feelings. For example we can understand one’s emotional state by analyzing his facial
expression. If we add these perceptual abilities of human beings to computers, it would
enable computers to work together with human beings as intimate partners. The BLUE EYES
TECHNOLOGY aims at creating computational machines that have perceptual
and sensory ability like those of human beings.

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ACKNOWLEDGEMENT
I express my sincere gratitude and thanks to Dr. Monowar H Bhuyan (Head of the
Department, Computer Science and Engineering), Mr. Bharadwaj Choudhury (Faculty in-
charge, Seminar) and my faculty mentor Miss Ankita Goyal for their valuable guidance,
support and kind co-operation during preparation of the seminar report and helping me
towards presenting the seminar successfully.
I also extend my sincere thanks to all other faculty members and staff of Computer Science
and Engineering Department and my friends for their support, help and encouragement.

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CONTENTS
Abstract………………………………………………………………………….ii
Acknowledgement……………………………………………………………...iii
List of Figures…………………………………………………………………..vi
1. Introduction………………………………………………………………….1
1.1 Meaning of Blue Eyes Technology………………………………….1
1.2 Key Features of the Blue Eyes System……………………………….2
2. System Overview……………………………………………………………..3
2.1 Hardware description…………………………………………………4
2.1.1 Data Acquistion Unit………………………………………...4
2.1.2 Central System Unit…………………………………………5
3. Emotion Computing…………………………………………………………..7
3.1 Facial Expression……………………………………………………..7
3.2Experimental Results of Ekman………………………………………8
3.3Affective Computing…………………………………………………8
4. Types of Emotion Sensors…………………………………………………..10
4.1 Hand…………………………………………………………………11
4.1.1 Emotion Mouse…………………………………………….11
4.2 Eye…………………………………………………………………..13
4.2.1 Manual and Gaze Input Cascaded (MAGIC) Pointing…….13
4.2.2 Eye tracking………………………………………………...19
4.3Voice…………………………………………………………………20
4.3.1Artificial Intelligent Speech Recognition…………………...20

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4.4 The Simple User Interest Tracker…………………………………..20
5. Applications of Blue Eyes Technology……………………………………..21
6. Advantages and Disadvantages of Blue Eyes Technology………………….23
6.1 Advantages………………………………………………………….23
6.2 Disadvantages……………………………………………………….23
8. Conclusion………………………………………………………………….24
Bibliography…………………………………………………………………...25

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LIST OF FIGURES
2.1 Overview of the system…………………………………………………..3
2.2 Detailed system diagram…………………………………………………4
2.3 DAU components………………………………………………………...5
4.1 Emotional Mouse………………………………………………………...11
4.2 System Configuration for Emotional Mouse…………………………….12
4.3 Different Signals Interpreted……………………………………………..12
4.4 The Liberal MAGIC Pointing Technique………………………………..16
4.5 The Conservative MAGIC Pointing Technique………………………….16
4.6 Eye Tracker………………………………………………………………19

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Chapter 1
Introduction
Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-
visuals and sensory information. Adding extraordinary perceptual abilities to computers
would enable computers to work together with human beings as intimate partners.
Researchers are attempting to add more capabilities to computers that will allow them to
interact like humans, recognize human presents, talk, listen, or even guess their feelings.
The BLUE EYES technology aims at creating computational machines that have perceptual
and sensory ability like those of human beings. It uses non-obtrusive sensing method,
employing most modern video cameras and microphones to identify the users actions through
the use of imparted sensory abilities [1].
The idea of giving computers personality or, more accurately, “emotional intelligence” may
seem creepy, but technologists claim such machines would offer some important
advantages. Despite their lightning speed and awesome powers of computation, today's PCs
are essentially deaf, dumb, and blind. They can't see us, they can't hear us, and they certainly
don't care a while how we feel. Every computer user knows the frustration of nonsensical
error messages, buggy software, and abrupt system crashes. We might berate the computer as
if it were an unruly child, but, of course, the machine can't respond. "It's ironic that people
feel like dummies in front of their computers, when in fact the computer is the dummy," says
Rosalind Picard, a computer science professor at the MIT Media Lab in Cambridge [2].
The U.S. computer giant, IBM has been conducting research on the Blue Eyes
technology at its Almaden Research Center (ARC) in San Jose, Calif., since 1997. The ARC
is IBM's main laboratory for basic research. The primary objective of the research is to give a
computer the ability of the human being to assess a situation by using the senses of sight,
hearing and touch.
1.1. Meaning of Blue Eyes Technology
The jargon ‘Blue Eyes’ involves Bluetooth technology and the movements of the eyes.
Bluetooth provides reliable wireless communication, whereas the eye movements enable us
to obtain a lot of interesting and important information. This required designing a Personal
Area Network linking all the operators and the supervising system.
This technology can enable the machine to understand what a user wants, where he is looking
at, and even realize his physical or emotional states. From the physiological data , an
emotional state may be determined which would then be related to the task the user is

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currently doing on the computer. Over a period of time, a user model will be built in order to
gain a sense of the user's personality. The scope of many upcoming projects in this area is to
have the computer adapt to the user in order to create a better working environment where the
user is more productive. Adding extraordinary perceptual abilities to computers would enable
computers to work together with human beings as intimate partners. Researchers are
attempting to add more capabilities to computers that will allow them to interact like humans,
recognize human’s presence, talk, listen, or even guess their feelings.
1.2. Key features of the Blue Eyes system
Blue Eyes system provides technical means for monitoring and recording human-operator's
physiological condition. The key features of the system are [1]:
 Visual attention monitoring (eye motility analysis)
 Physiological condition monitoring (pulse rate, blood oxygenation)
 Operator's position detection (standing, lying)
 Wireless data acquisition using Bluetooth technology
 Real-time user-defined alarm triggering Physiological data, operator's voice
and overall view of the control room recording
 Recorded data playback
For example, a Blue Eyes-enabled television could become active when the user makes eye
contact, at which point the user could then tell the television to "turn on".

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Chapter 2
System Overview
The most vital parameter in this system is associated with saccadic activity. Saccades are
rapid eye jumps to new locations within a visual environment assigned predominantly by the
conscious attention process. Such a concept can be helpful to the system in monitoring the
status of the operator’s visual attention along with head acceleration, which at times
accompanies large displacement of the visual axis (saccades larger than 15 degrees).
Blue Eyes system monitors the status of the operator’s visual attention through measurement
of saccadic activity. The system checks parameters like heart beat rate and blood oxygenation
against abnormal situations and triggers user defined alarms. Blue Eyes system consists of a
mobile measuring device and a central analytical system.
The mobile device is integrated with Bluetooth module providing wireless interface between
sensors worn by the operator and the central unit. ID cards assigned to each of the operators
and adequate user profiles on the central unit side provide necessary data personalization. As
such, the system consists of [3] [4]:
 Mobile measuring device (DAU)
 Central System Unit (CSU)
Fig 2.1: Overview of the system

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The detailed system diagram is shown below:
Fig 2.2: Detailed System diagram
2.1 Hardware description
2.1.1 Data Acquisition Unit (DAU)
Data Acquisition Unit is a mobile part of the Blue eyes system. Its main task is to fetch the
physiological data from the sensor and to send it to the central system to be processed.
An off-shelf eye movement sensor ‘JAZZ multi-sensor’ was used as physiological data
sensor. It supplies raw digital data regarding eye position and the level of blood oxygenation.
[3] [4].
Hardware specification of DAU: The Data Acquisition Unit comprises several hardware
modules:
 Atmel 89C52 microcontroller - system core
 Bluetooth module (based on ROK101008)
 HD44780 - small LCD display
 24C16 - I2C EEPROM (on a removable ID card)
 MC145483 – 13bit PCM codec
 Jazz Multi-sensor interface
 Beeper and LED indicators ,6 AA batteries and voltage level monitor

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Fig 2.3: DAU components
Atmel 8952 microcontroller to be the core of the Data Acquisition Unit since it is a well-
established industrial standard and provides necessary functionality (i.e. high speed serial
port) at a low price. The figure above shows the other DAU components.
Bluetooth module used in the this project supports synchronous voice data transmission
(SCO link) .Developers had decided to use hardware PCM codec to transmit operator’s voice
and central system sound feedback. Codec employed reduces the microcontroller’s tasks and
lessens the amount of data being sent over the UART.
2.1.2 Central System Unit
There are four main CSU modules (refer Fig 2.1): Connection Manager, Data Analysis, Data
Logger and Visualization.
1) Connection Manager
It is responsible for managing the wireless communication between the mobile Data
Acquisition Units and the central system. The Connection Manager handles:
 communication with the CSU hardware
 searching for new devices in the covered range
 establishing Bluetooth connections
 connection authentication
 incoming data buffering
 sending alerts
2) Data Analysis module

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It performs the analysis of the raw sensor data in order to obtain information about the
operator’s physiological condition. The separately running Data Analysis module
supervises each of the working operators. The module consists of a number of smaller
analyzers extracting different types of information. Each of the analyzers registers at the
appropriate Operator Manager or another analyzer as a data consumer and, acting as a
producer, provides the results of the analysis. The most important analyzers are:
 Saccade detector - monitors eye movements in order to determine the level of
operator's visual attention.
 Pulse rate analyzer - uses blood oxygenation signal to compute operator's pulse
rate.
 Custom analyzer - recognize other behaviors than those which are built-in the
system. The new modules are created using decision tree induction algorithm.
3) Data Logger Module
The module provides support for storing the monitored data in order to enable the
supervisor to reconstruct and analyze the course of the operator’s duty. The module
registers as a consumer of the data to be stored in the database. Apart from the raw or
processed physiological data, alerts and operator’s voice are stored. The raw data is
supplied by the related Operator Manager module, whereas the Data Analysis module
delivers the processed data. The voice data is delivered by a Voice Data Acquisition
module.
4) Visualization Module
The module provides user interface for the supervisors. It enables them to watch each of
the working operator’s physiological condition along with a preview of selected video
source and his related sound stream. All the incoming alarm messages are instantly
signaled to the supervisor. Moreover, the visualization module can be set in the off-line
mode, where all the data is fetched from the database. Watching all the recorded
physiological parameters, alarms, video and audio data the supervisor is able to
reconstruct the course of the selected operator’s duty.

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Chapter 3
Emotion Computing
3.1 Facial expression
According to a past study on facial expression [3], a correlation between a person’s emotional
state and a person’s physiological has been measured. Selected works from Ekman and others
on measuring facial behaviors describe Ekman’s Facial Action Coding System (Ekman and
Rosenberg, 1997). One of his experiments involved participants attached to devices to record
certain measurements including pulse, galvanic skin response (GSR), temperature, somatic
movement and blood pressure. He then recorded the measurements as the participants were
instructed to mimic facial expressions which corresponded to the six basic emotions. He
defined the six basic emotions as anger, fear, sadness, disgust, joy and surprise. Six
participants were trained to exhibit the facial expressions of the six basic emotions. While
each participant exhibited these expressions, the physiological changes associated with affect
were assessed. The measures taken were GSR, heart rate, skin temperature and general
somatic activity (GSA). These data were then subject to two analyses. For the first analysis, a
multidimensional scaling (MDS) procedure was used to determine the dimensionality of the
data. This analysis suggested that the physiological similarities and dissimilarities of the six
emotional states fit within a four dimensional model. For the second analysis, a discriminant
function analysis was used to determine the mathematic functions that would distinguish the
six emotional states. This analysis suggested that all four physiological variables made
significant, non-redundant contributions to the functions that distinguish the six states.
Moreover, these analyses indicate that these four physiological measures are sufficient to
determine reliably a person’s specific emotional state. Because of the need to incorporate
these measurements into a small, non-intrusive form, scientists explored taking these
measurements from the hand. The amount of conductivity of the skin is best taken from the
fingers. However, the other measures may not be as obvious or robust. It has also been
hypothesized that changes in the temperature of the finger are reliable for prediction of
emotion. Also hypothesis were laid that the GSA can be measured by change in movement in
the computer mouse.
Rosalind Picard (1997) describes why emotions are important to the computing community.
There are two aspects of affective computing: giving the computer the ability to detect
emotions and giving the computer the ability to express emotions. Not only are emotions
crucial for rational decision making as Picard describes, but emotion detection is an important
step to an adaptive computer system.
An adaptive, smart computer system has been driving our efforts to detect a person’s
emotional state. An important element of incorporating emotion into computing is for

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productivity for a computer user. A study (Dryer & Horowitz, 1997) has shown that people
with personalities that are similar or complement each other collaborate well.
Dryer (1999) has also shown that people view their computer as having a personality. For
these reasons, it is important to develop computers which can work well with its user.
3.2 Experimental Results of Ekman:
Ekman did rigorous experiments involving facial expressions of human subjects [3] [4] [7].
Subjects were asked to portray various commonly witnessed facial expressions. The data for
each subject consisted of scores for four physiological assessments [GSA, GSR, pulse, and
skin temperature, for each of the six emotions (anger, disgust, fear, happiness, sadness, and
surprise)] displayable through various facial expressions, across the five minute baseline and
test sessions. GSA data was sampled 80 times per second, GSR and temperature were
reported approximately 3-4 times per second and pulse was recorded as a beat was detected,
approximately 1 time per second.
To account for individual variance in physiology, we calculated the difference between the
baseline and test scores. Scores that differed by more than one and a half standard deviations
from the mean were treated as missing. By this criterion, twelve score were removed from the
analysis. The results show that the theory behind the Emotion mouse (discussed more later
on) work is fundamentally sound. The physiological measurements were correlated to
emotions using a correlation model. The correlation model is derived from a calibration
process in which a baseline attribute-to emotion correlation is rendered based on statistical
analysis of calibration signals generated by users having emotions that are measured or
otherwise known at calibration time.
3.3 Affective Computing
In affective computing [10], suggestions were imparted of separately examining functions
that are not so easily separated in humans. For example, the Macintosh has been displaying a
smile for years upon successful boot-up. But few people would confuse its smile – albeit an
emotional expression – with a genuine emotional feeling. Machines can fake the appearance
of an emotion quite well, without having any feelings similar to those we would have: They
can separate expression from feeling. With a machine it is easy to see how emotion
expression does not imply “having” the underlying feeling.
Researchers in the last decade have obtained dozens of scientific findings illuminating
important roles of emotion in intelligent human functioning, even when it looks like a person
is showing no emotion. These findings have reshaped scientific understanding of emotion and
have inspired a number of researchers to consider that emotional mechanisms might be more
valuable than previously believed. Consequently, a number of researchers have charged

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ahead with building machines that have several affective abilities, especially: recognizing,
expressing, modeling, communicating, and responding to emotion. And, within these areas, a
number of new criticisms and challenges have arisen.
Finally, affective computing has always emphasized the need for a balance. Some machines
won’t need any emotional abilities; while others might be improved with some subset of
them. There is a time to express emotion, and a time to forbear; a time to sense what others
are feeling and a time to ignore feelings. In every time, we need a balance, and this balance is
missing in computing. Designers of future computing can continue with the development of
computers that ignore emotions, or they can take the risk of making machines that recognize
emotions, communicate with them, and perhaps even “have” them, at least in the ways in
which emotions aid in intelligent interaction and decision making.

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Chapter 4
Types of Emotion Sensors
Blue Eyes technology uses most modern cameras, microphones and advanced non-obtrusive
sensing techniques to interact with humans and understand the emotions of human beings.
The machine has the ability to grasp the eye movement of the user, the needs of the user and
also can understand the emotional and physical states of a user in front of the machine. The
steps involved for designing such type of computers are given below.
1. Process of giving sensing capacity: Blue Eyes utilizes many sensor mechanisms,
which is equivalent for the ears, eyes and other sensory organs that human beings
used to express emotions and recognize each other. Blue Eyes uses voice recognition
software, cameras and biometric sensors to understand and respond to the emotional
levels of humans.
2. Human Emotion detection or Affect Detection: The Blue Eyes enables the machines to
identify these minor emotional variations of human beings even by a single touch on
the mouse or key board and the machines started to react with the users according to
this emotional levels. This is done with the guidance of intelligent devices like
“Emotion Mouse”. Along with this Emotion Mouse, Simple User Interest Tracker
(SUITOR) and Artificial Intelligent Speech Recognition are equipped with the Blue
Eyes technology to understand the speech and identify the interest of the peoples at
that instance of time.
For implementing the Affective Computing we need Emotion Sensors.
The different types of Emotion Sensors used in Blue Eyes Technology are as follows:
For Hand: Emotion Mouse
For Eyes: Magic Pointing, Eye Tracking
For Voice: Artificial Intelligence Speech Recognition

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4.1. Hand
4.1.1 Emotion Mouse
Fig 4.1: Emotional Mouse
One proposed, non-invasive method for gaining user information through touch is via a
computer input device, the mouse. This then allows the user to relate the cardiac rhythm, the
body temperature, electrical conductivity of the skin and other physiological attributes with
the mood. This has led to the creation of the “Emotion Mouse” [4] [5] [6]. The device can
measure heart rate, temperature, galvanic skin response and minute bodily movements and
matches them with six emotional states: happiness, surprise, anger, fear, sadness and disgust.
The mouse includes a set of sensors, including infrared detectors and temperature-sensitive
chips. These components, user researchers’ stress, will also be crafted into other commonly
used items such as the office chair, the steering wheel, the keyboard and the phone handle.
Integrating the system into the steering wheel, for instance, could allow an alert to be
sounded when a driver becomes drowsy.
 Information Obtained From Emotion Mouse
1) Behavior
a. Mouse movements
b. Button click frequency
c. Finger pressure when a user presses his/her button
2) Physiological information
a. Heart rate (Electrocardiogram (ECG/EKG), & Photoplethysmogram (PPG))
b. Skin temperature (Thermester)
c. Skin electricity (Galvanic skin response, GSR)

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d. Electromyographic activity (Electromyogram, MG)
 Prototype
Fig.4.2: System Configuration for Emotional Mouse
 Samples Obtained From Emotional Mouse
Fig.4.3: Different Signals interpreted

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4.2 Eye
4.2.1 Manual and Gaze Input Cascaded (MAGIC) Pointing
This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking
has long been considered as an alternative or potentially superior pointing method for
computer input. They believed that many fundamental limitations exist with traditional gaze
pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a
motor control task. They, therefore, proposed an alternative approach, dubbed MAGIC
(Manual And Gaze Input Cascaded) pointing [4] [5] [6] [7]. With such an approach, pointing
appears to the user to be a manual task, used for fine manipulation and selection. However, a
large portion of the cursor movement is eliminated by warping the cursor to the eye gaze
area, which encompasses the target. Two specific MAGIC pointing techniques, one
conservative and one liberal, were designed, analyzed, and implemented with an eye tracker
we developed. They were then tested in a pilot study. This early stage exploration showed
that the MAGIC pointing techniques might offer many advantages, including reduced
physical effort and fatigue as compared to traditional manual pointing, greater accuracy and
naturalness than traditional gaze pointing, and possibly faster speed than manual pointing.
The pros and cons of the two techniques are discussed in light of both performance data and
subjective reports.
In their view, there were two fundamental shortcomings to the existing gaze pointing
techniques, regardless of the maturity of eye tracking technology. First, given the one-degree
size of the fovea and the subconscious jittery motions that the eyes constantly produce, eye
gaze is not precise enough to operate UI widgets such as scrollbars, hyperlinks, and slider
handles.
At a 25-inch viewing distance to the screen, one degree of arc corresponds to 0.44 in, which
is twice the size of a typical scroll bar and much greater than the size of a typical character.
Second, and perhaps more importantly, the eye, as one of our primary perceptual devices, has
not evolved to be a control organ. Sometimes its movements are voluntarily controlled while
at other times it is driven by external events. With the target selection by dwell time method,
considered more natural than selection by blinking, one has to be conscious of where one
looks and how long one looks at an object. If one do not look at a target continuously for a set
threshold (e.g., 200 ms), the target will not be successfully selected. On the other hand, if one
stares at an object for more than the set threshold, the object will be selected, regardless of
the user’s intention. In some case there is not an adverse effect to a false target selection.
Other times it can be annoying and counter-productive (such as unintended jumps to a web
page). Furthermore, dwell time can only substitute for one mouse click. There are often two
steps to target activation. A single click selects the target (e.g., an application icon) and a
double click (or a different physical button click) opens the icon (e.g., launches an
application). To perform both steps with dwell time is even more difficult.

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In short, to load the visual perception channel with a motor control task seems fundamentally
at odds with users’ natural mental model in which the eye searches for and takes in
information and the hand produces output that manipulates external objects. Other than for
disabled users, who have no alternative, using eye gaze for practical pointing does not appear
to be very promising.
An interesting question arises that are there interaction techniques that utilize eye movement
to assist the control task but do not force the user to be overly conscious of his eye
movement. They wanted to design a technique in which pointing and selection remained
primarily a manual control task but were also aided by gaze tracking. Their key idea was to
use gaze to dynamically redefine (warp) the “home” position of the pointing cursor to be at
the vicinity of the target, which was presumably what the user was looking at, thereby
effectively reducing the cursor movement amplitude needed for target selection.
Once the cursor position had been redefined, the user would need to only make a small
movement to, and click on, the target with a regular manual input device. In other words, they
wanted to achieve Manual And Gaze Input Cascaded (MAGIC) pointing, or Manual
Acquisition with Gaze Initiated Cursor. There are many different ways of designing a
MAGIC pointing technique. Critical to its effectiveness is the identification of the target the
user intends to acquire. They have designed two MAGIC pointing techniques, one liberal and
the other conservative in terms of target identification and cursor placement. The liberal
approach is to warp the cursor to every new object the user looks at.
The user can then take control of the cursor by hand near (or on) the target, or ignore it and
search for the next target. Operationally, a new object is defined by sufficient distance (e.g.,
120 pixels) from the current cursor position, unless the cursor is in a controlled motion by
hand. Since there is a 120-pixel threshold, the cursor will not be warped when the user does
continuous manipulation such as drawing. Note that this MAGIC pointing technique is
different from traditional eye gaze control, where the user uses his eye to point at targets
either without a cursor or with a cursor that constantly follows the jittery eye gaze motion.
The liberal approach may appear “pro-active,” since the cursor waits readily in the vicinity of
or on every potential target. The user may move the cursor once he decides to acquire the
target he is looking at. On the other hand, the user may also feel that the cursor is over-active
when he is merely looking at a target, although he may gradually adapt to ignore this
behavior. The more conservative MAGIC pointing technique we have explored does not
warp a cursor to a target until the manual input device has been actuated. Once the manual
input device has been actuated, the cursor is warped to the gaze area reported by the eye
tracker. This area should be on or in the vicinity of the target. The user would then steer the
cursor annually towards the target to complete the target acquisition. As illustrated, to
minimize directional uncertainty after the cursor appears in the conservative technique, they
introduced an “intelligent” bias.

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Instead of being placed at the center of the gaze area, the cursor position is offset to the
intersection of the manual actuation vector and the boundary of the gaze area. This means
that once warped, the cursor is likely to appear in motion towards the target, regardless of
how the user actually actuated the manual input device.
It was hoped that with the intelligent bias the user would not have to Gaze position reported
by eye tracker Eye tracking boundary with 95% confidence True target will be within the
circle with 95% probability. The cursor is warped to eye tracking position, which is on or
near the true target previous cursor position, far from target (e.g., 200 pixels)

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Fig 4.4: The Liberal MAGIC Pointing Technique
Fig 4.5: The Conservative MAGIC Pointing technique (with “intelligent offset”)

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Both the liberal and the conservative MAGIC pointing techniques offer the following
potential advantages:
1. Reduction of manual stress and fatigue, since the cross screen long-distance cursor
movement is eliminated from manual control.
2. Practical accuracy level. In comparison to traditional pure gaze pointing whose accuracy is
fundamentally limited by the nature of eye movement, the MAGIC pointing techniques let
the hand complete the pointing task, so they can be as accurate as any other manual input
techniques.
3. A more natural mental model for the user. The user does not have to be aware of the role of
the eye gaze. To the user, pointing continues to be a manual task, with a cursor conveniently
appearing where it needs to be.
4. Speed. Since the need for large magnitude pointing operations is less than with pure
manual cursor control, it is possible that MAGIC pointing will be faster than pure manual
pointing.
5. Improved subjective speed and ease-of-use. Since the manual pointing amplitude is
smaller, the user may perceive the MAGIC pointing system to operate faster and more
pleasantly than pure manual control, even if it operates at the same speed or more slowly.
The fourth point wants further discussion. According to the well accepted Fitts’ Law, manual
pointing time is logarithmically proportional to the A/W ratio, where A is the movement
distance and W is the target size. In other words, targets which are smaller or farther away
take longer to acquire.
For MAGIC pointing, since the target size remains the same but the cursor movement
distance is shortened, the pointing time can hence be reduced. It is less clear if eye gaze
control follows Fitts’ Law. In Ware and Mikaelian’s study, selection time was shown to be
logarithmically proportional to target distance, thereby conforming to Fitts’ Law. To the
contrary, Silbert and Jacob found that trial completion time with eye tracking input increases
little with distance, therefore defying Fitts’ Law. In addition to problems with today’s eye
tracking systems, such as delay, error, and inconvenience, there may also be many potential
human factor disadvantages to the MAGIC pointing techniques we have proposed, including
the following:
1 With the more liberal MAGIC pointing technique, the cursor warping can be overactive at
times, since the cursor moves to the new gaze location whenever the eye gaze moves
more than a set distance (e.g., 120 pixels) away from the cursor. This could be
particularly distracting when the user is trying to read. It is possible to introduce
additional constraint according to the context. For example, when the user’s eye appears
to follow a text reading pattern, MAGIC pointing can be automatically suppressed.

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2 With the more conservative MAGIC pointing technique, the uncertainty of the exact
location at which the cursor might appear may force the user, especially a novice, to
adopt a cumbersome strategy: take a touch (use the manual input device to activate the
cursor), wait (for the cursor to appear), and move (the cursor to the target manually). Such
a strategy may prolong the target acquisition time. The user may have to learn a novel
hand-eye coordination pattern to be efficient with this technique. Gaze position reported
by eye tracker Eye tracking boundary with 95% confidence True target will be within the
circle with 95% probability The cursor is warped to the boundary of the gaze area, along
the initial actuation vector Previous cursor position, far from target Initial manual
actuation vector.
3 With pure manual pointing techniques, the user, knowing the current cursor location,
could conceivably perform his motor acts in parallel to visual search. Motor action may
start as soon as the user’s gaze settles on a target. With MAGIC pointing techniques, the
motor action computation (decision) cannot start until the cursor appears. This may
negate the time saving gained from the MAGIC pointing technique’s reduction of
movement amplitude. Clearly, experimental (implementation and empirical) work is
needed to validate, refine, or invent alternative MAGIC pointing techniques.

19
4.2.2 Eye Tracking
Since the goal of this work is to explore MAGIC pointing as a user interface technique, the
IBM Almaden Eye Tracker was used experimentally.
Fig 4.6 : Eye tracker
When the light source is placed on-axis with the camera optical axis, the camera is able to
detect the light reflected from the interior of the eye, and the image of the pupil appears
bright . This effect is often seen as the red-eye in flash photographs when the flash is close to
the camera lens. Bright (left) and dark (right) pupil images result from on- and off-axis
illumination. The glints, or corneal reflections, from the on- and off-axis light sources can be
easily identified as the bright points in the iris. The Almaden system uses two near infrared
(IR) time multiplexed light sou0rces, composed of two sets of IR LED’s, which were
synchronized with the camera frame rate. One light source is placed very close to the
camera’s optical axis and is synchronized with the even frames. Odd frames are synchronized
with the second light source, positioned off axis. The two light sources are calibrated to
provide approximately equivalent whole-scene illumination.

20
4.3. Voice
4.3.1 Artificial Intelligent Speech Recognition
It is important to consider the environment in which the speech recognition system has to
work. The grammar used by the speaker and accepted by the system, noise level, noise type,
position of the microphone, and speed and manner of the user’s speech are some factors that
may affect the quality of speech recognition. The user speaks to the computer through a
microphone, which, in used; a simple system may contain a minimum of three filters. The
more the number of filters used, the higher the probability of accurate recognition. Presently,
switched capacitor digital filters are used because these can be custom-built in integrated
circuit form. These are smaller and cheaper than active filters using operational amplifiers.
The filter output is then fed to the ADC to translate the analogue signal into digital word. The
ADC samples the filter outputs many times a second. Each sample represents different
amplitude of the signal. Each value is then converted to a binary number proportional to the
amplitude of the sample. A central processor unit (CPU) controls the input circuits that are
fed by the ADCS. A large RAM (random access memory) stores all the digital values in a
buffer area. This digital information, representing the spoken word, is now accessed by the
CPU to process it further. The normal speech has a frequency range of 200 Hz to 7 kHz.
Recognizing a telephone call is more difficult as it has bandwidth limitation of 300 Hz to3.3
kHz.
4.4 The Simple User Interest Tracker
Computers would have been much more powerful, had they gained perceptual and sensory
abilities of the living beings on the earth. What needs to be developed is an intimate
relationship between the computer and the humans. And the Simple User Interest Tracker
(SUITOR) is a revolutionary approach in this direction. By observing the webpage someone
is browsing, the SUITOR can help by fetching more information at his desktop. By simply
noticing where the user’s eyes focus on the computer screen, the SUITOR can be more
precise in determining his topic of interest. It can even deliver relevant information to a
handheld device.
IBM's Blue Eyes research project began with a simple question, according to Myron Flickner,
a manager in Almaden's USER group: Can we exploit nonverbal cues to create more effective
user interfaces? One such cue is gaze the direction in which a person is looking. Flickner and
his colleagues have created some new techniques for tracking a person's eyes and have
incorporated this gaze-tracking technology into two prototypes. One, called SUITOR (Simple
User Interest Tracker), fills a scrolling ticker on a computer screen with information related
to the user's current task. SUITOR knows where one is looking, what applications one is
running, and what Web pages one may be browsing. "If I'm reading a Web page about IBM,
for instance," says Paul Maglio, the Almaden cognitive scientist who invented SUITOR, "the
system presents the latest stock price or business news stories that could affect IBM.”

21
Chapter 5
Applications of Blue Eyes Technology
Some of the many applications of Blue Eyes Technology are discussed below:
i. A number of large retailers have implemented surveillance systems that record
and interpret customer movements, using software from Almaden's BlueEyes
research project [3][7][8]. Blue Eyes is developing ways for computers to
anticipate users' wants by gathering video data on eye movement and facial
expression. One’s gaze might rest on a Web site heading, for example, and that
would prompt his computer to find similar links and to call them up in a new
window. But the first practical use for the research turns out to be snooping on
shoppers. Blue Eyes software makes sense of what the cameras see to answer key
questions for retailers, including, How many shoppers ignored a promotion? How
many stopped? How long did they stay? Did their faces register boredom or
delight? How many reached for the item and put it in their shopping carts? Blue
Eyes works by tracking pupil, eyebrow and mouth movement. When monitoring
pupils, the system uses a camera and two infrared light sources placed inside the
product display. One light source is aligned with the camera's focus; the other is
slightly off axis. When the eye looks into the camera-aligned light, the pupil
appears bright to the sensor, and the software registers the customer's attention.
This is the way it captures the person's income and buying preferences. Blue Eyes
is actively been incorporated in some of the leading retail outlets.
ii. Another application would be in the automobile industry. By simply touching a
computer input device such as a mouse, the computer system is designed to be
able to determine a person's emotional state for cars, it could be useful to help
with critical decisions like: "I know you want to get into the fast lane, but I'm
afraid I can't do that. Your too upset right now" and therefore assist in driving
safely [3].
iii. Current interfaces between computers and humans can present information
vividly, but have no sense of whether that information is ever viewed or
understood. In contrast, new real-time computer vision techniques for perceiving
people allows us to create "Face-responsive Displays" and "Perceptive
Environments", which can sense and respond to users that are viewing them.
Using stereo-vision techniques, we are able to detect, track, and identify users
robustly and in real time. This information can make spoken language interface
more robust, by selecting the acoustic information from a visually-localized
source. Environments can become aware of how many people are present, what
activity is occuring, and therefore what display or messaging modalities are most
appropriate to use in the current situation [3].

22
iv. We could see its use in video games where, it could give individual challenges to
customers playing video games. Typically targeting commercial business. The
integration of children's toys, technologies and computers is enabling new play
experiences that were not commercially feasible until recently. The Intel Play
QX3 Computer Microscope, the Me2Cam with Fun Fair, and the Computer Sound
Morpher are commercially available smart toy products developed by the Intel
Smart Toy Lab in . One theme that is common across these PC-connected toys is
that users interact with them using a combination of visual, audible and tactile
input & output modalities. The presentation will provide an overview of the
interaction design of these products and pose some unique challenges faced by
designers and engineers of such experiences targeted at novice computer users,
namely young children.
v. The technology can also be used in Generic Control rooms as well as in Power
Stations, Security Centers, Flight Control Centers and Operation Theatres, where
the attention and contribution of human operator is, otherwise, entirely needed on
a whole time basis.

23
Chapter 6
Advantages and Disadvantages of Blue Eyes Technology
6.1 Advantages
The key features of the system are:
 visual attention monitoring (eye motility analysis).
 physiological condition monitoring (pulse rate, blood oxygenation).
 operator's position detection (standing, lying) ,wireless data acquisition using
Bluetooth technology.
 real-time user-defined alarm triggering.
 physiological data, operator's voice and overall view of the control room
recording.
 recorded data playback.
Blue Eyes system can be applied in every working environment requiring permanent
operator's attention:
 At power plant control rooms.
 At captain bridges.
 At flight control centers.
Data security - This system implies data security which is require in the modern network
system.
 Only registered mobile devices can connect to the system.
 Bluetooth connection authentication & encryption.
 Access rights restrictions.
 Personal and physiological data encryption.
6.2 Disadvantages
 Doesn’t predict nor interfere with operators thoughts.
 Cannot force directly the operator to work.
 Liberal approach is distracting when the user is trying to read.
 The motor action Computation until the cursor appears.

24
Chapter 7
Conclusion
Human has tremendous expectations from human being’s future and present. This tends to
innovate new and helpful technologies which can make the life more comfortable and
reliable. This technology is one of them that can make so. The BLUE EYES technology
ensures a convenient way of simplifying our life by providing more delicate and user friendly
facilities in computing devices. The whole approach is innovative since it helps supervise the
operator not the process, as it is in presently available solutions. We hope the system in its
commercial release will help avoid potential threats resulting from human errors, such as
weariness, oversight, tiredness or temporal indisposition.
Instead of using cumbersome modules to gather information about the user, it will be better
to use smaller and less intrusive units. The use of a miniature CMOS camera integrated into
the eye movement sensor will enable the system to calculate the point of gaze and observe
what the operator is actually looking at. Introducing voice recognition algorithm will
facilitate the communication between the operator and the central system and simplify
authorization process. Apart from considering instances of applications mentioned in the
report only, for example: the operators working in control rooms, our solution may well be
applied to everyday life situations.
These new possibilities can cover areas such as industry, transportation, military command
centers to even complex genetic and neurological research centers.

25
Bibliography
[1] Mehta G.N., Desai J.K., Chavda D.G., “Blue Eyes-Human-Operator Monitoring System”, IJSET,
vol. 1, Issue 3, pp. 91-95, July 2012.
[2] Blue Eyes Overview http://www.mepits.com/, accessed on 20/10/2015 at 8:30 AM
[3] Srivastava K.R.., Chaudhary K.A., Baldaniya H.J.., “Vision System of Blue Eyes”, International
Journal of Emerging Research in Management and Technology, vol. 3, Issue 4.
[4] Belhumeur P., Hespanha J., and Kriegman D., “Eigenfaces vs. Fisherfaces: Recognition
using class specific linear projection”, IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 19, July 1997.
[5] Jabrain B., Wu J., Vertegaal R., and Grigorov L., “Establishing remote conversations
through eye contact with physical awareness proxies”, Extended Abstracts of ACM CHI,
2003.
[6] Ekman P., Rosenberg, E., “What the Face Reveals: Basic and Applied Studies of
Spontaneous Expression Using the Facial Action Coding System”, Oxford University
Press, New York.
[7] Blue Eyes Technology http://www.seminarsonly.com/, accessed on 20/10/2015 at 11:30 AM.
[8] Article: Blue Eyes http:// www.slideshare.net/deepu427/blue eyes technology accessed on
21/10/2015 at 8:15 PM.
[9] Article: Blue Eyes http:// www.pediain.com/seminar/Blue-Eyes-Technology-Seminar accesed on
2/10/15 at 8:00 PM
[10] R. W. Picard M.I.T Media Laboratory Perceptual Computing Section Technical Report
No. 321 accessed on 22/10/15 at 8.15 PM.

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