Biometrics Technology Seminar Report.

Biometrics Technology Pavan Kumar M.T.
Visvesvaraya Technological University
Santhibastawad Road, Machhe
Belgaum-590 014, Karnataka
SEMINAR REPORT
On
“BIOMETRICS TECHNOLOGY”
Submitted in partial fulfillment of the requirements for the degree of
Bachelor of Engineering in Electronics & Communications prescribed by
Visvesvaraya Technological University.
Submitted by
PAVAN KUMAR M.T. 1VK06EC045
Under The Guidance Of
Smt. Vidya E.V.
Asst. Professor, Dept. of TE.
VKIT, Bangalore
DEPARTMENT OF ELECTRONICS & COMMUNICATION ENGINEERING
VIVEKANANDA INSTITUTE OF TECHNOLOGY
GUDIMAVU, KUMBALGODU, BANGALORE-560 074
Dept. of E&C, VKIT 2010 1

Janatha Education Society (Regd.)
VIVEKANANDA INSTITUTE OF TECHNOLOGY
Gudimavu, Kumbalgodu, Kengeri Hobli, Bangalore-560 074.
DEPARTMENT OF ELECTRONICS & COMMUNICATION
ENGINEERING
Certificate
This is to Certify that the seminar work entitled BIOMETRICS TECHNOLOGY is a
bonafied work carried out by Pavan Kumar M.T. bearing USN 1VK06EC045 in partial
fulfillment for the award of degree of Bachelor of Engineering in Electronics &
communication of the Visvesvaraya Technological University, Belgaum during
the year 2009-10 It is certified that all corrections/suggestions indicated for
internal assessment have been incorporated in the report deposited in the library.
The seminar report has been approved, as it satisfies the academic requirements in
respect of Seminar Work prescribed for the Bachelor of Engineering Degree.
Signature of the Guide Signature of the HOD
Smt. Vidya E.V. Prof.K.V.MahendraPrashanth

Acknowledgement
The satisfaction and euphoria that accompany the successful completion of any task
would be incomplete without the people who made it possible, whose constant guidance and
encouragement crowned our efforts with success.
My primary thanks goes to our beloved Principal Dr. Doddanna Hemanth, Vivekananda
Institute of Technology, Bangalore for his kind support that he has provided throughout this
effort.
I would like to thank Prof. K.V.Mahendra Prasanth, HOD, Department of EC/TE,
Vivekananda Institute of Technology, Bangalore for his valuable guidance and assistance.
I would like to thank my guide Smt. Vidya E.V., Asst. Professor, Department of TE,
Vivekananda Institute of Technology, Bangalore for her suggestions, guidance and
encouragement in successful completion of this technical task.
Finally, I would like to immensely thank my parents and all my friends for their constant
support and encouragement which was the fuel propelling my effort.
PAVAN KUMAR M.T.

Abstract
In this Hi-Tech era, there is a great demand to identify and authenticate the
individuals. Till now we are totally dependent upon Passwords and Pin Numbers for
identification. How secure are passwords? With the numerous passwords that an individual has
to remember, they are often forgotten, misplaced, or stolen. Think of how many different
passwords you have to remember: computer passwords, internet site logons and passwords, PIN
numbers for the ATM and for credit cards, the list goes on. We are arriving at a conclusion that
these technologies are not sufficient for the security of an individual as these are hard to
remember, easily transferable, easily stolen and there are many weaknesses. Due to these
weaknesses biometrics came into existence.
Biometrics is that study of science that deals with personal human behavioral and
physiological characteristics and such as fingerprints, handprints, iris scanning, voice scanning,
face recognition and signature recognition. These technologies are far more promising than that
which are used currently to identify an individual. This paper highlights some of the benefits and
the few limitations of using biometrics for authentication .With biometrics it doesn’t matter if
we forget your password or lose your smart card.

Contents
1. Chapter 1 : Introduction and History 1
1.1 Introduction 1
1.2 History 2
2. Chapter 2 : Block Diagram of Biometric System 4
3. Chapter 3 : Classification of Biometrics 6
3.1 Fingerprint 6
3.2 Face Recognisation 8
3.3 Hand Geometry 12
3.4 Iris Recognisation 13
3.5 Speaker Recognisation 14
3.6 Signature Recognisation 16
3.7 Gesture Recognisation 17
3.8 Multimodal Biometrics 18
4. Chapter 4 : System Accuracy and Comparison 19
4.1 System Accuracy 19
4.2 Comparison of Biometrics Technology 20
5. Chapter 5 : Applications 21
5.1 Eye-gazed System 21
5.2 Televisions Controlled by Hand Gestures 22
5.3 Mimi Switch 22
5.4 Controller Free Gaming 22
6. Conclusion and Future Works 24
References 2

Chapter 1
Introduction and History
1.1 Introduction:-
The term "biometrics" is derived from the Greek words bio means “life” and
metric means “to measure”.
BIOMETRICS refers to the automatic identification of a person based on his
physiological / behavioral characteristics. This method of identification is preferred for
various reasons; the person to be identified is required to be physically present at the
point of identification; identification based on biometric techniques obviates the need to
remember a password or carry a token.
A biometric is a unique, measurable characteristic or trait for automatically
recognizing or verifying the identity of a human being. Biometrics is a powerful
combination of science and technology that can be used to protect and secure our most
valuable information and property.
With the increased use of computers or vehicles of information technology, it is
necessary to restrict access to sensitive or personal data. By replacing PINs, biometric
techniques can potentially prevent unauthorized access to fraudulent use of ATMs,
cellular phones, smart cards, desktop PCs, workstations, and computer networks. PINs
and passwords may be forgotten, and token based methods of identification like passports
and driver’s licenses may be forged, stolen, or lost .Thus biometric systems of
identification are enjoying a renewed interest.
Recognisation requires the system to look through many stored sets of
characteristics and pick the one that matches the unknown individual being presented.
Various types of biometric systems are being used for real–time identification; the most
popular are based on face recognition and fingerprint matching. However there are
other biometric systems that utilize iris and retinal scan, speech, gesture recognisation,
and hand geometry.
Biometric technologies are becoming the foundation of an extensive array of
highly secure identification and personal verification solutions. The basic idea behind
biometrics is that our bodies contain unique properties that can be used to distinguish us
from others. A biometric system is essentially a pattern recognition system, which

makes a personal identification by determining the authenticity of a specific physiological
or behavioral characteristics possessed by the user.
An important issue in designing a practical system is to determine how an
individual is identified. Depending on the context, a biometric system can be either a
verification (authentication) system an identification system. Verification involves
confirming or denying a person’s claimed identity. In identification one has to establish a
person’s identity. Identification systems based on biometrics are important building
blocks in simplifying our interaction with the myriad digital systems and devices that we
are all using in increasing numbers.
There are levels of security from the most basic to the most robust with biometrics
being the most secure:
 Something that you have - such as an ID badge with a photograph on it.
 Something that you know - such as a password or PIN number.
 Something which you are - such as biometric data – fingerprints, iris, voice or
face scans.
Figure 1: Explains the meaning of definition
Biometrics is rapidly evolving technology, which is being used in forensics such
as criminal identification and prison security, and has the potential to be used in a large
range of civilian application areas. Biometrics can be used transactions conducted via
telephone and Internet (electronic commerce and electronic banking). In automobiles,
biometrics can replace keys with key -less entry devices.

1.2 History:-
When we talk about biometric history, we would realize that since time
immemorial people always tried their best to use some way or the other so that they could
identify one person from another, whether it was through footprints or tattoos or
photos. Biometric history indicates that the science did not originate at a single place.
People all over the world were using the basics for mainly identifying individuals from
each other.
The ancient Egyptians and the Chinese played a large role in biometrics' history.
Although biometric technology seems to belong in the twenty-first century, the history of
biometrics goes back thousands of years. Possibly the most primary known instance of
biometrics in practice was a form of finger printing being used in China in the 14th
century, as reported by explorer Joao de Barros. Barros wrote that the Chinese merchants
were stamping children's palm prints and footprints on paper with ink so as to
differentiate the young children from one another. This is one of the most primitive
known cases of biometrics in use and is still being used today.
Bertillon developed a technique of multiple body measurements which later got
named after him “Bertillonage”. His method was then used by police authorities
throughout the world, until it quickly faded when it was discovered that some people
shared the same measurements and based on the measurements alone, two people could
get treated as one. After the failure of Bertillonage, the police started using finger
printing, which was developed by Richard Edward Henry of Scotland Yard, essentially
reverting to the same methods used by the Chinese for years.
Commercial advancements for biometric devices began in the 1970s when a
system called Identimat which measured the shape of the hand and length of the fingers
was used as part of a time clock at Shearson Hamill, a Wall Street investment firm.
Subsequently, hundreds of Identimat devices were used to establish identity for physical
access at secure facilities run by Western Electric, U.S. Naval Intelligence, the
Department of Energy, and U.S. Naval Intelligence and like organizations.

Chapter 2
Block Diagram of Biometric System
Biometric devices consist of a reader or scanning device, software that converts
the gathered information into digital form, and a database that stores the biometric data
for comparison with previous records. When converting the biometric input, the software
identifies specific points of data as match points. The match points are processed using an
algorithm into a value that can be compared with biometric data in the database.
The biometric feature must have the following characteristics:-
(a) Universality, which means that every person should have the characteristic,
(b) Uniqueness, two persons should not have the same term or measurement of
Characteristic.
(c) Permanence, the characteristic should be invariant with time.
(d) Measurability, the characteristic can be quantified that is the origin of the Cameras
used in biometric systems are generally either CCD (charge couple device) or CMOS
(combined metal oxide semiconductor) image sensors. CCD is comparatively more costly
than CMOS.
Figure 2: Basic block diagram of biometrics system

The main operations a system can perform are enrollment and test. During the
enrollment, biometric information from an individual is stored. During the test, biometric
information is detected and compared with the stored information. Note that it is crucial
that storage and retrieval of such systems themselves be secure if the biometric system is,
robust.
The first block (sensor) is the interface between the real world and the system; it
has to acquire all the necessary data. Most of the times it is an image acquisition system,
but it can change according to the characteristics desired. A sample of the biometric trait is
captured, processed by a computer, and stored for later comparison.
The second block performs all the necessary pre-processing: it has to remove
artifacts from the sensor, to enhance the input (e.g. removing background noise), to use
some kind of normalization, etc.
In the third block features needed are extracted. This step is an important step as
the correct features need to be extracted and the optimal way. A vector of numbers or an
image with particular properties is used to create a template. A template is a synthesis of
all the characteristics extracted from the source, in the optimal size to allow for adequate
identifiability. All Biometric authentications require comparing a registered or enrolled
biometric sample (biometric template or identifier) against a newly captured biometric
sample.
If enrollment is being performed where the biometric system identifies a person
from the entire enrolled population by searching a database for a match based solely on
the biometric. For example, an entire database can be searched to verify a person has not
applied for entitlement benefits under two different names. This is sometimes called
“one-to-many” matching.
If a verification phase is being performed, the biometric system authenticates a
person’s claimed identity from their previously enrolled pattern. This is also called “one-
to-one” matching. The obtained template is passed to a matcher that compares it with
other existing templates. The matching program will analyze the template with the input.
This will then be output for any specified use or purpose.

Chapter 3
Classification of Biometrics
Biometrics encompasses both physiological and behavioral characteristics. A
physiological characteristic are related to the shape of a body. A relatively stable
physical feature such as finger print, hand geometry, iris pattern or facial features.
These factors are basically unalterable without trauma to the individual.
Behavioral tracts, on the other hand, are related to the behavior of a person. The
most common trait used in identification is a person’s signature. Other behaviors used
include a person’s keyboard typing, gait and speech patterns. Most of the behavioral
characteristics change over time.
Some of physical biometrics is
 Fingerprint - analyzing fingertip patterns.
 Facial Recognition - measuring facial characteristics.
 Hand Geometry - measuring the shape of the hand.
 Iris recognition - analyzing features of colored ring of the eye.
Some of behavioral biometrics is
 Speaker Recognition - analyzing vocal behavior.
 Signature Recognisation - analyze the physical activity of signing.
 Gesture Recognisation - analyzing the motions of body.
3.1 Fingerprint:-
Humans have used fingerprints for personal identification for many centuries and
the matching accuracy using fingerprints has been shown to be very high. Fingerprinting
is probably the best-known biometric- method of identification used for 100 years. There
are a few variants of image capture technology available for such commercially oriented
fingerprint sensor, including optical, silicon, ultrasound, thermal and hybrid.
Among all the biometric techniques, fingerprint-based Identification is the oldest
method that has been successfully used in numerous applications. Everyone is known to
have unique, immutable fingerprints. A fingerprint is made of a series of ridges and

furrows on the surface of the finger as shown in the fig 3.1.1. The uniqueness of a
fingerprint can be determined by the pattern of ridges and furrows as well as minutiae
points. Minutiae points are the local ridge characteristics that occur either at a ridge
ending or a ridge bifurcation. A ridge ending is defined as the point where the ridge ends
abruptly and the ridge bifurcation is the point where the ridge splits into two or more
branches.
When a user places their finger on the terminals scanner the image is
electronically read, analyzed, and compared with a previously recorded image of the same
finger which has been stored in the database. The imaging process is based on digital
holography, using an electro-optical scanner about the size of a thumbprint. The scanner
reads three-dimensional data from the finger such as skin undulations, and ridges and
valleys, to create a unique pattern that is composed into a template file.
Figure 3: Fingerprint classification of 6 categories (a) arch, (b) tented arch, (c) right loop,
(d) left loop, (e) whorl, and (f) twin loop
An algorithm is developed to classify fingerprints into five classes, namely, whorl,
right loop, arch and tented arch as shown in figure 3. Critical points in a finger print,
called core and delta are marked on one of the fingers as shown in figure 3 (c). The core
is the inner point, normally in the middle of the print, around which swirls, loops, or
arches center. It is frequently characterized by a ridge ending and several acutely curved
ridges. Deltas are the points, normally at the lower left and right hand of the fingerprint,
around which a triangular series of ridges center. The algorithm separates the number of
ridges present in four directions (o degree, 45 degree, 90 degree and 135 degree) by
filtering the central part of a fingerprint with a bank of Gabor filters. This information is
quantized to generate a finger code which is used for classification. To avoid fake-finger

attacks, some systems employ so-called liveness detection technology, which takes
advantage of the sweat activity of human bodies. High-magnification lenses and special
illumination technologies capture the finger’s perspiration and pronounce the finger dead
or alive.
3.1.1 Advantages:-
 Fingerprint recognition equipment is relatively low-priced compared to other
biometric system.
 Fingerprints are unique to each finger of each individual and the ridge
arrangement remains permanent during one's lifetime.
3.1.2 Disadvantages:-
 Some people have damaged or eliminated fingerprints.
 Vulnerable to noise and distortion brought on by dirt and twists.
3.2 Face Recognisation:-
Face recognition technology analyze the unique shape, pattern and positioning of
the facial features. Face recognition is very complex technology and is largely software
based. Face recognition starts with a picture, attempting to find a person in the image.
This can be accomplished using several methods including movement, skin tones, or
blurred human shapes. The face recognition system locates the head and finally the eyes
of the individual. A matrix is then developed based on the characteristics of the
individual’s face. The method of defining the matrix varies according to the algorithm
(the mathematical process used by the computer to perform the comparison). This matrix
is then compared to matrices that are in a database and a similarity score is generated for
each comparison.
Despite the fact that there are more reliable biometric recognition techniques such
as fingerprint and iris recognition, these techniques are intrusive and their success
depends highly on user cooperation, since the user must position her eye in front of the
iris scanner or put her finger in the fingerprint device. On the other hand, face recognition
is non-intrusive since it is based on images recorded by a distant camera, and can be very
effective even if the user is not aware of the existence of the face recognition system. The
human face is undoubtedly the most common characteristic used by humans to recognize

other people and this is why personal identification based on facial images is considered
the friendliest among all biometrics.
Face has certain distinguishable landmarks that are the peaks and valleys that sum
up the different facial features. There are about 80 peaks and valleys on a human face.
The following are a few of the peaks and valleys that are measured by the software:
 Distance between eyes
 Width of nose
 Depth of eye sockets
 Cheekbones
 Jaw line
 Chin
These peaks and valleys are measured to give a numerical code, a string of
numbers, which represents the face in a database. This code is called a face print. Face
recognition involves the comparison of a given face with other faces in a database with
the objective of deciding if the face matches any of the faces in that database.
Figure 4: Face nodal points
Image matching usually involves three steps:
1. Detection of the face in a complex background and localization of its exact
position,
2. Extraction of facial features such as eyes, nose, etc, followed by normalization to
align the face with the stored face images, and
3. Face classification or matching.

In addition, a face recognition system usually consists of the following four modules:
1. Sensor module, which captures face images of an individual. Depending on the
sensor modality, the acquisition device maybe a black and white or color camera,
a 3D sensor capturing range (depth) data, or an infrared camera capturing infrared
images.
2. Face detection and feature extraction module. The acquired face images are first
scanned to detect the presence of faces and find their exact location and size. The
output of face detection is an image window containing only the face area.
Irrelevant information, such as background, hair, neck and shoulders, ears, etc are
discarded.
3. Classification module, in which the template extracted during step 2, is compared
against the stored templates in the database to generate matching scores, which
reveal how identical the faces in the probe and gallery images are. Then, a
decision-making module either confirms (verification) or establishes
(identification) the user’s identity based on the matching score. In case of face
verification, the matching score is compared to a predefined threshold and based
on the result of this comparison; the user is either accepted or rejected. In case of
face identification, a set of matching scores between the extracted template and
the templates of enrolled users is calculated. If the template of user X produces the
best score, then the unknown face is more similar to X, than any other person in
the database. To ensure that the unknown face is actually X and not an impostor,
the matching score is compared to a predefined threshold.
4. Sometimes, more than one template per enrolled user is stored in the gallery
database to account for different variations. Templates may also be updated over
time, mainly to cope with variations due to aging.
Face detection algorithms can be divided into three categories according to
1. Knowledge-based methods are based on human knowledge of the typical human
face geometry and facial features arrangement. Taking advantage of natural face
symmetry and the natural top-to-bottom and left-to-right order in which features
appear in the human face, these methods find rules to describe the shape, size,

texture and other characteristics of facial features (such as eyes, nose, chin,
eyebrows) and relationships between them (relative positions and distances). A
hierarchical approach may be used, which examines the face at different
resolution levels. At higher levels, possible face candidates are found using a
rough description of face geometry. At lower levels, facial features are extracted
and an image region is identified as face or non-face based on predefined rules
about facial characteristics and their arrangement.
2. Feature invariant approaches aim to find structural features that exist even when
the viewpoint or lighting conditions vary and then use these to locate faces.
Different structural features are being used: facial local features, texture, and
shape and skin color. Local features such as eyes, eyebrows, nose, and mouth are
extracted using multi-resolution or derivative filters, edge detectors,
morphological operations or thresholding. Statistical models are then built to
describe their relationships and verify the existence of a face. Neural networks,
graph matching, and decision trees were also proposed to verify face candidates.
3. Template-based methods. To detect a face in a new image, first the head outline,
which is fairly consistently roughly elliptical, is detected using filters or edge
detectors. Then the contours of local facial features are extracted in the same way,
exploiting knowledge of face and feature geometry.
More recently, techniques that rely on 3D shape data have been proposed. 3D face
recognition is a modality of facial recognition methods in which the three-dimensional
geometry of the human face is used. 3D face recognition has the potential to achieve
better accuracy than its 2D counterpart by measuring geometry of rigid features on the
face. This avoids such pitfalls of 2D face recognition algorithms as change in lighting,
different facial expressions, make-up and head orientation.
4.2.1 Advantages:-
 No contact required.
 Commonly available sensors (cameras).

 Face can be obstructed by hair, glasses, hats, scarves etc.
 Difficult to distinguish between twins.
 Sensitive to changes in lighting, expression, and poses faces changeover time.
3.3 Hand Geometry:-
Hand geometry recognition systems are based on a number of measurements
taken from the human hand, including its shape, size of palm, and lengths and widths of
the fingers. The technique is very simple, relatively easy to use, and inexpensive.
Environmental factors such as dry weather or individual anomalies such as dry skin do
not appear to have any negative effects on the verification accuracy of hand geometry-
based systems. The geometry of the hand is not known to be very distinctive and hand
geometry based recognition systems cannot be scaled up for systems requiring
identification of an individual from a large population. Further, hand geometry
information may not be invariant during the growth period of children. In addition, an
individual's jewelry (e.g., rings) or limitations in dexterity (e.g., from arthritis), may pose
further challenges in extracting the correct hand geometry information. The physical size
of a hand geometry-based system is large, and it cannot be embedded in certain devices
like laptops.
Figure 5: Hand geometry system
3.3.1 Advantages:-
 Easy to capture.
 The major advantage is that most people can use it and as such, the acceptance
rate is good.
 Believed to be a highly stable pattern over the adult lifespan.

 Use requires some training.
 System requires a large amount of physical space.
3.4 Iris Recognisation:-
The iris of each eye of each person is absolutely unique. In the entire human
population, no two irises are alike in their mathematical detail. This even applies to
identical twins. The iris of each eye is protected from the external environment. It is
clearly visible from a distance, making it ideal for a biometric solution. Image acquisition
for enrolment and recognition is easily accomplished and most importantly is non-
intrusive.
The Iris Code creation process starts with video-based image acquisition. This is a
purely passive process achieved using CCD (Charge Coupled Device) Video Cameras.
This image is then processed and encoded into an Iris Code record, which is stored in an
Iris Code database. This stored record is then used for identification in any live
transaction when an iris is presented for comparison.
Figure 6 : Iris scan process
The iris-scan process begins with a photograph. A specialized camera, typically
very close to the subject, no more than three feet, uses an infrared imager to illuminate the
eye and capture a very high-resolution photograph. This process takes only one to two
seconds and provides the details of the iris that are mapped, recorded and stored for future
matching/verification.
Eyeglasses and contact lenses present no problems to the quality of the image and
the iris-scan systems test for a live eye by checking for the normal continuous fluctuation
in pupil size.

The inner edge of the iris is located by an iris-scan algorithm which maps the iris
distinct patterns and characteristics. An algorithm is a series of directives that tell a
biometric system how to interpret a specific problem. Algorithms have a number of steps
and are used by the biometric system to determine if a biometric sample and record is a
match.
Iris is composed before birth and, except in the event of an injury to the eyeball,
remains unchanged throughout an individual’s lifetime. Iris patterns are extremely
complex, carry an astonishing amount of information and have over 200 unique spots.
The fact that an individual’s right and left eyes are different and that patterns are easy to
capture, establishes iris-scan technology as one of the biometrics that is very resistant to
false matching and fraud.
The false acceptance rate for iris recognition systems is 1 in 1.2 million,
statistically better than the average fingerprint recognition system. The real benefit is in
the false-rejection rate, a measure of authenticated users who are rejected. Fingerprint
scanners have a 3 percent false-rejection rate, whereas iris scanning systems boast rates at
the 0 percent level.
3.4.1 Advantages:-
 Iris recognition is very accurate with very low false acceptance rate
 Complex procedure.
 High cost.
3.5 Speaker Recognition:-
Speaker, or voice, recognition is a biometric modality that uses an individual’s
voice for recognition purposes. The speaker recognition process relies on features
influenced by both the physical structure of an individual’s vocal tract and the behavioral
characteristics of the individual. A popular choice for remote authentication due to the
availability of devices for collecting speech samples and its ease of integration, speaker
recognition is different from some other biometric methods in that speech samples are
captured dynamically or over a period of time, such as a few seconds. Analysis occurs on

a model in which changes over time are monitored.
Voice recognition technology utilizes the distinctive aspects of the voice to verify
the identity of individuals. Voice recognition is occasionally confused with speech
recognition, a technology which translates what a user is saying (a process unrelated to
authentication). Voice recognition technology, by contrast, verifies the identity of the
individual who is speaking. The two technologies are often bundled – speech recognition
is used to translate the spoken word into an account number, and voice recognition
verifies the vocal characteristics against those associated with this account.
Voice recognition can utilize any audio capture device, including mobile and land
telephones and PC microphones. The performance of voice recognition systems can vary
according to the quality of the audio signal as well as variation between enrollment and
verification devices, so acquisition normally takes place on a device likely to be used for
future verification. During enrollment an individual is prompted to select a passphrase or
to repeat a sequence of numbers. Voice recognition can function as a reliable
authentication mechanism for automated telephone systems, adding security to automated
telephone-based transactions in areas such as financial services and health care. Certain
voice recognition technologies are highly resistant to imposter attacks, means that voice
recognition can be used to protect reasonably high-value transactions.
Figure 7: Voice Sample
Speech samples are waveforms with time on the horizontal axis and loudness on
the vertical access. The speaker recognition system analyzes the frequency content of the
speech and compares characteristics such as the quality, duration, intensity dynamics, and
pitch of the signal.

Voice recognition techniques can be divided into categories depending on the type
of authentication domain.
• Fixed text method is a technique where the speaker is required to say a predetermined
word that is recorded during registration on the system.
• In the text dependent method the system prompts the user to say a specific word or
phrase, which is then computed on the basis of the user’s fundamental voice pattern.
• The text independent method is an advanced technique where the user need not
articulate any specific word or phrase. The matching is done by the system on the basis of
the fundamental voice patterns irrespective of the language and the text used.
3.5.1 Advantages:-
 Simple and cost-effective technological application.
 Can be used for remote authentication.
 Voice and language usage change over time (e.g. as a result of age or illness).
3.6 Signature Recognisation:-
Biometric signature recognition systems measure and analyze the physical activity
of signing. Important characteristics include stroke order, the pressure applied, the pen-up
movements, the angle the pen is held, the time taken to sign, the velocity and acceleration
of the signature. Some systems additionally compare the visual image of signatures,
though the focus in signature biometrics lies on writer-specific information rather than
visual handwritten content. While it may appear trivial to copy the appearance of a
signature, it is difficult to mimic the process and behavior of signing.
Figure 8: Signature trait

Signature data can be captured via pens that incorporate sensors or through touch-
sensitive surfaces which sense the unique signature characteristics. Touch-sensitive
surfaces are increasingly being used on ICT devices such as screens, pads, mobile phones,
laptops and tablet PCs.
3.6.1 Advantages:-
 Main uses of signature biometrics include limiting access to restricted documents
and contracts, delivery acknowledgement and banking/finance related
applications.
 A person’s signature changes over time as well as under physical and emotional
influences.
3.7 Gesture Recognisation System:-
Gesture is the use of motions of the limbs or body as a means of expression,
communicate an intention or feeling. Gesture recognition enables humans to interface
with the machine (HMI) and interact naturally without any mechanical devices. Using the
concept of gesture recognition, it is possible to point a finger at the computer screen so
that the cursor will move accordingly. This could potentially make conventional input
devices such as mouse, keyboards and even touch-screens redundant. The ability to track
a person's movements and determine what gestures they may be performing can be
achieved through various tools. Although there is a large amount of research done in
image/video based gesture recognition, there is some variation within the tools and
environments used between implementations. In order to capture human gestures by
visual sensors, robust computer vision methods are also required, for example for hand
tracking and hand posture recognition or for capturing movements of the head, facial
expressions or gaze direction. The input devices of gesture recognisation system are
 Depth-aware cameras: Using specialized cameras such as time-of-flight
cameras, one can generate a depth map of what is being seen through the camera
at a short range, and use this data to approximate a 3d representation of what is

being seen. These can be effective for detection of hand gestures due to their short
range capabilities.
 Stereo cameras: Using two cameras whose relations to one another are known, a
3d representation can be approximated by the output of the cameras. To get the
cameras' relations, one can use a positioning reference such as
an infrared emitters.
 Controller-based gestures: These controllers act as an extension of the body so
that when gestures are performed, some of their motion can be conveniently
captured by software. Mouse gestures are one such example
 Single camera: A normal camera can be used for gesture recognition where the
resources/environment would not be convenient for other forms of image-based
recognition. Although not necessarily as effective as stereo or depth aware
cameras, using a single camera allows a greater possibility of accessibility to a
wider audience.
4.8.1 Advantages:-
 A new interactive Technology.
 Eliminates the use of mechanical devices.
 Complex
 High costs
3.8 Multimodal Biometrics System:-
Multimodal biometric systems are those that utilize more than one physiological
or behavioral characteristic for enrollment, verification, or identification. A biometric
system which relies only on a single biometric identifier in making a personal
identifications often not able to meet the desired performance requirements. Identification
based on multiple biometrics represents on emerging trend. A multimodal
biometric system is introduced which integrates face recognition, fingerprint
verification, and speaker verification in making a personal identification. This system

takes advantage of the capabilities of each individual biometric. It can be used to
overcome some of the limitations of a single biometrics.
Chapter 4
System Accuracy and Comparison
4.1 System Accuracy:-
Accuracy or performance of biometric systems is measured with three factors:
1. False acceptance rate (FAR)
2. False rejection rate (FRR)
3. Equal Error Rate (EER)
1. False Acceptance Rate:-
False acceptance rate is also known as Type I error. It measures the percentage of
impostors being incorrectly accepted as genuine user. Since almost all biometric systems
aim to achieve correct identity authentication, this number should be as low as possible.
2. False Rejection Rate:-
False rejection rate is also known as Type II error, this measures the percentage of
genuine users being incorrectly rejected. In order to minimize inconveniences (or
embarrassment) to the genuine user, this number should also be low.
3. Equal Error Rate:-
FAR and FRR are inversely related and a consolidation of the FAR and FFR is the
point at which accept and reject errors are equal. This is described as the equal error rate
(EER), sometimes also known as the cross-over error rate (CER). Low EER scores
generally indicate high levels of accuracy. This is illustrated in Figure 9. FAR and FFR
can often be adjusted by changing system parameters (rejection thresholds) or better
control of conditions under which systems are used (dust free, good lighting and so on).

Figure 9: System Accuracy Curve
4.2 Comparison of Biometric Technologies:-
Biometrics Universa
lity
Unique
ness
Permane
nce
Collectabil
ity
Performan
ce
Acceptabil
ity
Circumve
ntion
Fingerprint M H H M H M H
Face H L M H L H L
Hand
geometry
M M M H M M M
Iris H H H M H L H
Voice M L L M L H L
H-High, M-Medium-Low
Table 1: Comparison of Biometrics Technology
In the above table, universality indicates how common the biometric is found in
each person; uniqueness indicates how well the biometric separates one person from the
other; permanence indicates how well the biometric resist the effect of aging; while
collectability measures how easy it is to acquire the biometric for processing.
Performance indicates the achievable accuracy, speed and robustness of the biometrics
while acceptability indicates the degree of acceptance of the technology by the public in
their daily life and circumvention indicates the level of difficulty to circumvent or fool the
system into accepting an impostor.

Chapter 5
APPLICATIONS
5.1 Eye Gaze System:-
The Eye gaze Edge uses the pupil-center/corneal-reflection method to determine
where the user is looking on the screen. An infrared-sensitive video camera, mounted
beneath the System's screen, takes 60 pictures per second of the user's eye. A low power,
infrared light emitting diode (LED), mounted in the center of the camera's lens illuminates
the eye. The LED reflects a small bit of light off the surface of the eye's cornea. The light
also shines through the pupil and reflects off of the retina, the back surface of the eye, and
causes the pupil to appear white. The bright-pupil effect enhances the camera's image of the
pupil so the system's image processing functions can locate the center of the pupil.
The Edge calculates the person's gaze point, i.e., the coordinates of where he is looking on
the screen, based on the relative positions of the pupil center and corneal reflection within
the video image of the eye. Typically the Eye gaze Edge predicts the gaze point with an
average accuracy of a quarter inch or better. Prior to operating the eye tracking applications,
the Eye gaze Edge must learn several physiological properties of a user's eye in order to be
able to project his gaze point accurately. The system learns these properties by performing a
Figure 10: Display Panel of Eye-gazed System
calibration procedure. The user calibrates the system by fixing his gaze on a small circle
displayed on the screen, and following it as it moves around the screen. The calibration
procedure usually takes about 15 seconds, and the user does not need to recalibrate if he
moves away from the Eye gaze Edge and returns later. A user operates the Eye gaze System
by looking at rectangular keys that are displayed on the control screen. To "press" an Eye
gaze key, the user looks at the key for a specified period of time. The gaze duration required

to visually activate a key, typically a fraction of a second, is adjustable. An array of menu
keys and exit keys allow the user to navigate around the Eye gaze programs independently.
5.2 Television Controlled by Hand Gestures:-
Hitachi launched a high-end TV panel working with the Canesta 3D sensor, which
allows viewers interact with the TV controls via hand gestures. While the TV
displays 3D images we can wave our hand to power up the TV or move our hand circularly
to change the channel. Canesta’s 3D sensor is immune to lighting extremes and works in
any environment, whether it is indoors or outdoors, with the condition that we have to be
within the 3-meter working range. It also distinguished between one hand and two hands
and offers multiple commands depending on your hand’s motion. As we move our hands,
the 3D sensor developed with CMOS chip technology sends a stream of 3D data at 30
frames per second to the TVs micro-controller, where the gesture-recognition software
translates the depth maps into gestures and then into commands.
5.3 Mimi Switch:-
Mimi switch uses infrared sensors to measure movements inside the ear, which are
triggered by various facial expressions, and then transmits signals to a micro-computer that
controls electronic devices. It’s pretty much a hands-free remote control for anything
electronic. It stores and can even interpret data, allowing it to customize itself to individual
users, if it judges that we aren’t smiling enough, it may play a cheerful song.” In addition to
its usefulness in controlling music devices or cell phones, it can also be used as a safety
measure, providing hearing aids for the elderly, or health monitors: It could measure, say,
how often someone sneezes, and if it senses a serious health problem, it could send a
warning message to relatives.
5.4 Controller Free Gaming:-
Project Natal is the code name for a "controller-free gaming and entertainment
experience" by Microsoft for the Xbox 360 video game platform. Project Natal enables
users to control and interact with the Xbox 360 without the need to touch a game
controller through a natural user interface using gestures, spoken commands or presented
objects and images. The depth sensor consists of an infrared projector combined with a
monochrome CMOS sensor, and allows the Project Natal sensor to see in 3D under
any ambient light conditions. The sensing range of the depth sensor is adjustable, with the

Project Natal software capable of automatically calibrating the sensor based on game play
and the player's physical environment, such as the presence of chairs.
Project Natal is likely based on software technology developed internally by
Microsoft and 3D camera technology by Israeli developer Prime Sense, which interprets
3D scene information from a continuous infrared pattern. It was initially reported that the
hardware was acquired from time-of-flight camera developer 3DV Systems. Project
Natal enables advanced gesture recognition, facial recognition, and voice
recognition. The skeletal mapping technology was capable of simultaneously tracking up
to four users for motion analysis with a feature extraction of 48 skeletal points on a
human body at a frame rate of 30hertz. Depending on the person's distance from the
sensor, Project Natal is capable of tracking models that can identify individual fingers.
Figure 11: Project Natal by Microsoft
Biometrics is basically used in door lock systems and can be used to prevent
unauthorized access to ATMs, cellular phones, desktop PCs. It has largely used in access
control and identity verifications, including time and attendance

Conclusion and Future Works
Conclusion:-
Biometric is an emerging area with many opportunities for growth. Biometrics is
widely being used because of its user friendliness, flexibility in specifying required
security level and long term stability. The technology will continue to improve and
challenges such as interoperability solved through standardization. This will lead to
increase in the market adoption rate and the technology will proliferate. Possibly in the
near future, you will not have to remember PINs and passwords and keys in your bags or
pockets will be things of the past.
Future works:-
The future of biometrics holds great promise for law enforcement applications, as well
for private industry uses. Biometrics’ future will include e-commerce applications for extra
security on the checkout page, and biometrics will guard against unauthorized access to cars
and cell phones. In the future, biometric technology will further develop 3-D infrared facial
recognition access control, real-time facial recognition passive surveillance, and visitor
management authentication systems. Already A4Vision, a provider of 3-D facial scanning and
identification software uses specialized algorithms to interpret the traditional 2-D camera
image and transfer it into a 3-D representation of a registered face. This makes it almost
impossible to deceive the biometric system with still photos or other images. Strengthening
existing biometric innovations for future growth all of these security innovations will make
biometric technology more accurate and make its usage more widespread.

References:-
1. S. Prabhakar, S. Pankanti, and A. K. Jain, “Biometric Recognition: Security and
Privacy Concerns”, IEEE Security and Privacy Magazine, Vol. 1, No. 2, pp. 33-
42, 2003.
2. Jain, A. K.; Ross, Arun; Prabhakar, Salil (January 2004), "An introduction to
biometric recognition", IEEE Transactions on Circuits and Systems for Video
Technology 14th (1): 4–20, doi:10.1109/TCSVT.2003.818349
3. N. K. Ratha, J. H. Connell, and R. M. Bolle, "Enhancing security and privacy in
biometrics-based authentication systems," IBM systems Journal, vol. 40, pp. 614-
634, 2001.
4. A. Jain et al: BIOMETRICS: Personal Identification in NetworkedSociety, Kluwer
Academic Publishers, 1999, ISBN0-7923-8345-1.
5. M.Pantic and L.J.M. Rothkrantz, 'Towards an Affect-Sensitive Multimodal
Human-Computer Interaction '. In: Proceedings of the IEEE, vol. 91, no. 9, pp.
1370-1390, September 2003
6. A. Mehrabian, “Communication without words,” Psychol. Today, vol. 2, no. 4, pp.
53–56, 1968.
7. Jain, A., Bolle, R. and Pankanti S. (1999). BIOMETRICS: Personal Identification
in Networked Society. Kluwer Academic Publishers.
8. http:// www.biometrics.org/

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