ear biometrics(2)

8/8/2019 Ear Biometrics(2)

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RECENT ADVANCES IN EAR BIOMETRICS

INTRODUCTION

DEPARTMENT OF INFORMATION TECHNOLOGY 1


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CHAPTER 1

INTRODUCTION

In recent years, biometrics has been receiving a lot of attention. Biometrics plays a role

in almost every aspect of new security measures - from control access point to terrorist

identification. Unfortunately, most biometrics systems nowadays do not live up to their

expectations, usually due to the requirement of well controlled environments or other reasons. As

a result, researchers are actively searching for other means of human recognition for standalone

applications or used cooperatively with other biometrics technology in multimodal environments

in order to enhance the overall system reliability. One of the emerging candidates is Ear

Biometrics.

Try a simple experiment; try to visualize what your ears look like. You were not able

to? Well, then try to describe the ears of someone you see everyday. You will find that even if

you are looking directly at someone's ears, they are still difficult to describe. We simply do not

have the vocabulary for it; our everyday language provides only a few adjectives which can be

applied to ears, all of which are generic adjectives like large or floppy and not ones which are

solely1 used to describe ears. On the other hand, we are all capable of describing the faces of

even briefly glimpsed strangers with significant detail to allow police artists to reconstruct

remarkable resemblances of them. Even though we apparently lack the means to recognize one

another from our ears, we will see that the rich structure of the ear is unique and that it can be

used as an effective biometrics.



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WHAT IS BIOMETRICS?



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CHAPTER 2

WHAT IS BIOMETRICS?

Biometrics comprises methods for uniquely recognizing humans based upon one or moreintrinsic physical or behavioral traits.

Biometric characteristics can be divided in two main classes:

Physiological are related to the shape of the body. Examples include, but are not limited

to fingerprint, face recognition, DNA, Palm print, hand geometry, iris recognition, which

has largely replaced retina, and odor/scent.

Behavioral are related to the behavior of a person. Examples include, but are not limited

to typing rhythm, gait, and voice. Some researchershave coined the term behaviometrics

for this class of biometrics.

2.1 MODES OF OPERATION

A biometric system can operate in the following two modes:

Verification A one to one comparison of a captured biometric with a stored template to

verify that the individual is who he claims to be. Can be done in conjunction with a smart

card, username or ID number.

Identification A one to many comparison of the captured biometric against a biometric

database in attempt to identify an unknown individual.



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CHAPTER 3

APPLICATIONS

3.1 PHYSICAL ACCESS CONTROL

Physical access control biometrics includes everything that requires identity

authentication by scanning a person's unique physical characteristics. It is used where high

security is a necessity Hospitals, police, the military as well as the financial industry all use

physical access biometrics for the purpose of greater security and efficiency.

3.2 LOGICAL ACCESS CONTROL

Logical access control refers to electronic access controls whose purpose is to limit access

to data files and computer programs to individuals with the genuine authority to access such

information. Militaries and governments use logical access biometrics to protect their large and

powerful networks and systems which require very high levels of security. It is essential for the

large networks of police forces and militaries w

3.3 JUSTICE AND LAW ENFORCEMENT

Biometrics technology authenticates an individual's identity automatically, and has several

useful applications within Justiceand Law Enforcement. Biometric technology has the ability to

recognize fingerprint, iris, voice, facial recognition, hand, palm or skin. Biometric authentication

is greatly superior to card, token or password systems which can be stolen or counterfeited.



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3.4 HEALTHCARE BIOMETRICS

We hear all the time about the mistakes that are made within our healthcare system these

days. Records are mixed up, medical charts are confused, the wrong medication is given to the

wrong patient. Someone who shouldn't get their hands on your medical information does. There

is a desperate race going on to find the best method of securing your data and preventing

mistakes with consequences that range from embarrassing to deadly.

Finally, a solution has been found. Biometrics has revolutionized the healthcare security

industry. Biometrics is the study and analysis of biological data. Devices can take unique

information about you from your eye, or your hand print, or your thumb print and use it to

identify you. This information can be used to ensure that you are who you say you are, and youhave permission to be working with the healthcare information you are trying to access.

3.5 BORDER CONTROL/ AIRPORTS

Within databases of Biometric information can also be electronic reads of passports. For

identity management at Border Control/ Airports there exist two tier identity verificationprocesses, which can read passports, provide an electronic profile for the individual and organize

with accompanying fingerprint/iris scan etc. to create the most accurate identification profiles in

the world.

Passport reading technology is optical Biometrics, which scans the image of the passport

and databases the enclosed information. This is especially useful for identity management for

Border Control/ Airports where the systems can authenticate travel documents provide reference

to a traveler profile at the same time ensuring the security of the traveler's sensitive information.



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3.6 FINANCIAL AND TRANSACTIONAL SECURITY

Financial and transactional security, and identity theft protection are more of an issue

today than ever before! Have you ever panicked because you lost your wallet, with all of your

ATM and credit cards in it? Do you fear to bank via internet banking because you're worried

that your information will be stolen, along with your money?

Do you look over your shoulder three times before entering your PIN at the ATM machine to

make absolutely sure no one can see which numbers you're pressing? Does online shopping

worry you because you're scared someone will get your credit card number? Would you feel

much safer if there was a more fool proof way of conducting financial transactions that was more

secure than 4 numbers?

Let's say you're standing at the ATM, and enter not only your PIN, but also a thumb print or

voice scan before you get money. What if, when you go into the bank, they ask you not only for

a piece of identification but also an image of your iris? Thus, there is no risk of security.



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OVERVIEW



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CHAPTER 4

OVERVIEW

Although [1] a newcomer in the biometrics field, ears have long been used as a means

of human identification in the forensic field. Traditional and manual methods for description of

ear features and ear identification have been developed for more than 10 years .During crime

scene investigation, ear marks are often used for identification in the absence of(valid)

fingerprints. Just like fingerprints, the long-held history of the use of ear shapes/marks suggests

its use for automatic human identification. An ear recognition system is very much like a typical

face recognition system and consists of five components: image acquisition, preprocessing,

feature extraction, model training and template matching.

During image acquisition, an image of the ear is captured, usually with a CCD camera.

Although other methods such as the use of range sensors are also adopted, 2D image data as

input remains the mainstream choice. For preprocessing, standard techniques such as histogram

equalization and normalization are often used. As for feature extraction and model training,

different approaches vary greatly as they are directly related to the modeling of the ear and the

way of interpretation. Lastly, the template matching stages is largely the same and standard

statistical error analysis adopted.



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A SIMPLE BIOMETRIC SYSTEM



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CHAPTER 5

A SIMPLE BIOMETRIC SYSTEM

The first time an individual uses a biometric system is called an enrollment. During

the enrollment, biometric information from an individual is stored. In subsequent uses,

biometric information is detected and compared with the information stored at the time of

enrollment. Note that it is crucial that storage and retrieval of such systems themselves be

secure if the biometric system is to be 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. The second block performs all

the necessary pre-processing: it has to remove artifacts from the sensor, to enhance the


5.1 Simple Biometric System Block Diagram


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input (e.g. removing background noise), to use some kind of normalization, etc. In the

third block necessary features are extracted. This step is an important step as the correct

features need to be extracted in 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 the relevant

characteristics extracted from the source. Elements of the biometric measurement that are

not used in the comparison algorithm are discarded in the template to reduce the file size

and to protect the identity of the enrollee.

If enrollment is being performed, the template is simply stored somewhere (on a

card or within a database or both). [6]If a matching phase is being performed, the

obtained template is passed to a matcher that compares it with other existing templates,

estimating the distance between them using any algorithm (e.g. Hamming distance). Thematching program will analyze the template with the input. This will then be output for

any specified use or purpose (e.g. entrance in a restricted area).



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COMPARISON WITH OTHER BIOMETRICS



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CHAPTER 6

COMPARISON WITH OTHER BIOMETRICS

6.1 FACE

Among the list of popular, appearance-based biometrics, ear biometrics resembles face most.

Both of them make use of facial features and naturally face the same problem, namely

illumination, occlusion and head rotation. In the absence of a well-controlled environment,

illumination can varies dramatically in different image acquisition tries. This is especially true

for the ear, which possesses a more prominent contour than the face. The auricle may cast

shadows on other parts of the ear, which in itself a kind of occlusion. Just as a face maybe

covered with a scarf, the ears maybe partially or completely covered by hair or ear muffles. This

implies that cooperation of the subject is required in order to acquire an acceptable ear image for

registration. This requirement may impose a restriction on the use of ear biometrics in non

cooperative scenarios. (e.g. terrorist identification). Since the ear have a much small surface area

than the face, a small degree of head rotation may cause a significant displacement in the

captured ear image. Despite the fact that ear biometrics face the same intrinsic problem as face

biometrics, it does have its advantages over face. For instance, ear requires a smaller image size

under the same resolution , which may imply smaller computational load. Also, the ear has a

more uniform distribution of color and less variability with expressions . These attributes are

favorable for pattern recognition in general.

6.2 IRIS

In order to be less intrusive, the capturing device is usually placed far from the subject.

Added to the fact that the iris is much smaller than the ear, a high resolution camera is required

in order to acquire image of acceptable quality. Iris recognition also can fail when the subject

wear glasses.

6.3 FINGERPRINT

While ear biometrics requires the use of an ordinary CCD camera, fingerprint recognition

requires the use of specially designed sensors which maybe too expensive for large scale



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deployment. Unlike fingerprint recognition, [7]ear recognition is contact-less, which is less

intrusive and even essential for non-cooperative scenarios. This contact-less nature also reduces

the chance of wearing/damage of the capturing device. Although ridges and valleys of the

fingerprint are considered in feature extraction, the modeling of a fingerprint relies only on the

2D data captured. This is inherently different from ear, of which the 3D structure can be made

use of.

6.4 VOICE

The voice quality of the subject can[8] vary greatly with his health condition, while the

appearance of ear is almost invariant with respect to health. Voice recognition also suffers from

background noise.



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PRINCIPAL METHODS



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CHAPTER 7

PRINCIPAL METHODS

Although[10] ear recognition is a relatively new topic, researchers have already come up

with various approaches which drastically differ from each other in terms of acquisition, raw

data interpretation and feature extraction. Some of them are proved practice in the field of human

recognition (e.g. PCA, graph modal etc.), while some present a whole new perspective. In this

section, a brief introduction is given to each of these methods.

7.1 GRAPH MODEL

Burge and Burger were the first[9] to investigate the human ear as a biometric in the context

of machine vision. Inspired by the earlier work of Iannarelli , they conducted a proof of concept

study where the viability of the ear as a biometric was shown both theoretically in terms of the

uniqueness and measurability over time, and in practice through the implementation of a

computer vision based system. Each subject's ear was modeled as an adjacency graph built from

the Voronoi diagram of its Canny extracted curve segments. They devised a novel graph

matching algorithm for authentication which takes into account the erroneous curve segments

which can occur in the ear image due to changes such as lighting, shadowing, and occlusion.

They found that the features are robust and could be reliably extracted from a distance.

There are 5 stages in graph model. They are Acquisition, Localization, Edge Extraction,

Curve Extraction and Graph model.



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1.Acquisition: A 300 by 500 grayscale[11] image is taken of the subject's head in profile using

a CCD camera. Next the location of the ear in the image must be found. Fortunately, a number of

techniques from face localization are applicable. Two particularly promising methods for still

images are the application of Iconic Filter Banks and Fischerface . When sequences of color

images are available then color and motion based segmentation can be used to locate the subject

before applying ear localization. Since our goal was to construct a proof of concept system, we

used a relatively simple method based on deformable contours.

2.Localization: The ear is located by using deformable contours on a Gaussian pyramid

representation of the image gradient.

3. Edge extraction: Edges are computed using the Canny operator and thresholding with

hysteresis using upper and lower thresholds of 46 and 20 (7.1.2(b)).


Acquisition

Localization

Edge Extraction

Curve Extraction

Graph Model

Figure 7.1.1 Block Diagram For Graph Model


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4. Curve extraction: Edge relaxation is used to form larger curve segments, after which the

remaining small curve segments (i.e., length less than 10) are removed as is shown in 7.1.2(b).

We could attempt to[3] perform identification at this stage by trying to match features computed

from the extracted curves to those computed from the model. Differences in lighting and

positioning would render such a method very unreliable. What is needed is a description of the

relations between the curves in a way which is first invariant to affine transformations and

secondly invariant to small changes in the shape of the curves resulting from differences in

illumination. To achieve invariance under affine transformations, we turn to the neighboring

relation, and construct a Voronoi neighborhood graph of the curves and use it as our model.

5. Graph model: A generalized Voronoi diagram of the curves is built and a Neighborhood

graph is extracted (7.1.2(c)).

Using the above steps results in a high FRR due to variations in the graph models due to

underlying differences in the spatial relations of the extracted curves . To improve the FRR rate,

we first eliminate some of the erroneous curves and then develop a new matching process which

takes into account broken curves.

Error Correcting Graph Matching Algorithm

Let G(V,E) denote the graph model with each vertex V containing unary features of a curve

and edges E containing binary features between two neighboring curves. Matching is done by

searching for subgraph isomorphisms between the subject's stored graph Gs and the extracted

graph Gs'. If the distance d(Gs,Gs) between them is less than the established acceptance

threshold t then identification is verified.



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Voronoi diagram

The basic Voronoi diagram describes the areas that are nearest to a set of given points. These

can be viewed as zones of control[12], which can be used (for example) to help you find your

nearest supermarket. The partitioning of a plane with points into convex polygons such that

each polygon contains exactly one generating point and every point in a given polygon is closer

to its generating point than to any other. A Voronoi diagram is sometimes also known as

Dirichlet tessellation. The cells are called Dirichlet regions, Thiessen polytopes, or Voronoi

polygons.


Figure 7.1.2 Stages In Building Ear Biometric Graph Model


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7.2 PRINCIPAL COMPONENT ANALYSIS

A major problem in mining scientific data sets is that the data is often high dimensional,

that is, for each object, there are a large number of features representing the object. When the

number of dimensions reaches hundreds or even thousands, the computational time for the

pattern recognition algorithms can become prohibitive. This can be a problem, especially when

some of the features are not discriminatory. [2] In addition to the computational cost, irrelevant

features may also cause a reduction in the accuracy of some algorithms. For example,

experiments with a decision tree classifier have shown that adding a random binary feature to

standard datasets can deteriorate the classification performance by 5 - 10%. Further, in many

pattern recognition tasks, the number of features represents the dimension of a search space - the

larger the number of features, greater the dimension of the search space, and harder the problem.

The high dimensionality of scientific datasets can also be a problem in storage and retrieval.

For example, cluster analysis is often used to improve the way in which the storage of the data is

organized. If the dataset has a high dimension, clustering can become a problem.

To address this problem of high dimensionality, a common approach is to identify the most

important features associated with an object so that further processing can be simplified without

compromising the quality of the final results. There are several different ways in which the

dimension of a problem can be reduced. The simplest approach is to identify important attributes

based on input from domain experts. Another commonly used approach is Principal Component

Analysis (PCA) which defines new attributes (principal components or PCs) as mutually-

orthogonal linear combinations of the original attributes. For many datasets, it is sufficient to

consider only the first few PCs, thus reducing the dimension. However, for some datasets, PCA

does not provide a satisfactory representation.



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7.2.1 PCA TRANSFORMATION

Principal component analysis (PCA) rotates the original data space such that the axes of the

new coordinate system point into the directions of highest variance of the data. The axes or new

variables are termed principal components (PCs) and are ordered by variance: The first

component, PC 1, represents the direction of the highest variance of the data. The direction of the

second component, PC 2, represents the highest of the remaining variance orthogonal to the first

component. This can be naturally extended to obtain the required number of components which

together span a component space covering the desired amount of variance.

Since components describe specific directions in the data space, each component depends by

certain amounts on each of the original variables: Each component is a linear combination of all

original variables.


Figure 7.2.1 A 3d Image Converted To 2d By PCA


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7.2.2 DIMENSIONALITY REDUCTION

Low variance can often be assumed to represent undesired background noise. The

dimensionality of the data can therefore be reduced, without loss of relevant information, by

extracting a lower dimensional component space covering the highest variance. Using a lower

number of principal components[13] instead of the high-dimensional original data is a common

pre-processing step that often improves results of subsequent analyses such as classification.

For visualization, the first and second component can be plotted against each other to obtain

a two-dimensional representation of the data that captures most of the variance (assumed to be

most of the relevant information), useful to analyze and interpret the structure of a data set.

7.2.3 MATRIX ALGEBRA

This section serves to provide a background for the matrix algebra required in PCA.

Specifically I will be looking at eigenvectors and eigenvalues of a given matrix.

Example 1

Example 2



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7.2.4 EIGEN VECTORS

As you know, you can multiply two matrices together, provided they are compatible sizes.

Eigenvectors are a special case of this. Consider the two multiplications between a matrix and a

vector in example1.

In the first example, the resulting vector is not an integer multiple of the original vector,

whereas in the second example, the example is exactly 4 times the vector we began with. Why is

this? Well, the vector is a vector in 2 dimensional space. The vector (from the second example

multiplication) represents an arrow pointing from the origin, (0,0) to the point (3,2) . The other

matrix, the square one, can be thought of as a transformation matrix. If you multiply this matrix

on the left of a vector, the answer is another vector that is transformed from its original position.

It is the nature of the transformation that the eigenvectors arise from. Imagine a

transformation matrix that, when multiplied on the left, reflected vectors in the line y=x. Then

you can see that if there were a vector that lay on the line y=x, its reflection it itself. This vector

(and all multiples of it, because it wouldnt matter how long the vector was), would be an

eigenvector of that transformation matrix.

What properties do these eigenvectors have? You should first know that eigenvectors can

only be found for square matrices. And, not every square matrix has eigenvectors. And, given an

n*n matrix that does have eigenvectors, there are n of them. Given a 3*3 matrix, there are 3

eigenvectors.

Another property of eigenvectors is that even if I scale the vector by some amount before I

multiply it, I still get the same multiple of it as a result, as in example 2. This is because if you

scale a vector by some amount, all you are doing is making it longer, not changing its direction.

Lastly, all the eigenvectors of a matrix are perpendicular, ie. at right angles to each other, no

matter how many dimensions you have. By the way, another word for perpendicular, in maths

talk, is orthogonal. This is important because it means that you can express the data in terms of

these perpendicular eigenvectors, instead of expressing them in terms of the x and y axes.



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Another important thing to know is that when mathematicians find eigenvectors, they like to

find the eigenvectors whose length is exactly one. This is because, as you know, the length of a

vector doesnt affect whether its an eigenvector or not, whereas the direction does. So, in order

to keep eigenvectors standard, whenever we find an eigenvector we usually scale it to make it

have a length of 1, so that all eigenvectors have the same length. Heres a demonstration from

our example above.

7.2.5 EIGENVALUES

Eigenvalues are closely related to eigenvectors, in fact, we saw an eigenvalue in example1

Notice how, in both those examples, the amount by which the original vector was scaled after

multiplication by the square matrix was the same? In that example,the value was 4. 4 is the

eigenvalue associated with that eigenvector. No matter what multiple of the eigenvector we took

before we multiplied it by the square matrix, we would always get 4 times the scaled vector as

our result (as in example 2).

So you can see that eigenvectors and eigenvalues always come in pairs. When you get a fancy

programming library to calculate your eigenvectors for you, you usually get the eigenvalues as

well.



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Principal component analysis (PCA, also known as eigenfaces), which is a

dimensionality-reduction technique in which variation in the dataset is preserved. The

classification is done in eigenspace, which is a lower dimension space defined by principal

components or the eigenvectors of the data set.

The process consists of three steps:

i) Preprocessing

ii) Normalization

iii) Identification

In the preprocessing step the ear images are cropped to a size of 400x500 pixels (face

images to 768x1024). Coordinates of two distinct points are supplied to the normalization

routine: Triangular Fossa and the Antitragus. The normalization step includes geometric

normalization, masking and photometric normalization. In this phase all the images are scaled to

a standard 130x150 size. Next all non-ear areas, like hair, background etc. are masked. Different


ROW IMAGE

PREPROCESSING

NORMALIZATION

TRAINING

TESTING

RESULT

Figure 7.2.2 PCA Process


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levels of masking are experimented for finding the best one to get as good performance as

possible for the algorithm. Finally the images are normalized for illumination.

There are two phases in the identification phase: training and testing. In the training phase the

eigenvalues and eigenvectors of the training set are extracted and the eigenvectors are chosen

based on the top eigenvalues. Training set is a set of clean images without any duplicates. In the

testing phase the algorithm is provided a set of known ears and faces and a set of unknown ears

and faces as the probe set. The algorithm matches each probe to its possibly identity in the

gallery.

7.2.6 DRAWBACK OF EAR BIOMETRICS

The main drawback of ear biometrics is that they are not usable when the ear of the

subject is covered. In the case of active identification systems, this is not a drawback

as the subject can pull his hair back and proceed with the authentication process. The

problem arises during passive identification as in this case no assistance on the part of the subject

can be assumed.This problem can be solved with the help of a Thermogram Imagery.

7.3 THERMOGRAM IMAGERY

In the case of the ear being only partially [4]occluded by hair, it is possible to recognize the

hair and segment it out of the image. This can be done using texture and color segmentation, or

as we have implemented it, using thermogram images. A thermogram image is one in which the

surface heat (i.e., infrared light) of the subject is used to form an image. 7.3.1 is a thermogram of

the external ear. The subjects hair in this case has an ambient temperature between 27.2 and

29.7 degrees Celsius, while the pinna (i.e., the external anatomy of the ear) ranges from 30.0 to

37.2 degrees Celsius. Removing partially occluding hair is done by segmenting out the low

temperature areas which lie within the pinna. The Meatus (i.e., the passage leading into the inner

ear) of the ear is easily localizable using the thermogram imagery. In a profile image of a subject,

if the ear is visible, then the Meatus will be the hottest part of the image, with an expected

8degree Celsius temperature differential between it and the surrounding hair. In Fig 7.3.1, the



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Meatus is the clearly visible section in the temperature range of 34.8 to 37.2 degrees Celsius. By

searching for this high temperature area, it is possible to detect and localize ears using

thermograms.

Electromagnetic spectral bands below the visible spectrum such as X-rays and ultraviolet

radiation are harmful to the human body and are therefore unsuitable for ear recognition

applications. The spectral bands above the visible spectrum, such as thermal IR imagery, has

been suggested as an alternative source of information for the case of face recognition and ear

recognition . The visual spectrum ranges from 0.4 to 0.7 microns, which is the range in which a

visual camera can measure the electromagnetic energy. The infrared spectrum comprises the

reected IR and the thermal IR wavebands. The thermal IR band is associated with thermal

radiation emitted by the objects. The amount of emitted radiation depends on both the

temperature and the emissivity of the material. There are two primary bands in the thermal IR

spectrum: the mid-wave infrared (MWIR) and long-wave infrared (LWIR). Between these bands

there is a strong atmospheric absorption band where imaging becomes extremely difficult. The

human body emits thermal radiation in both these bands of the thermal IR spectrum in which

thermal IR cameras can sense temperature variations at a distance. Thermograms can be

produced and presented in the form of heatmapped 2D images.



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7.3.1 ADVANTAGES OF THERMOGRAM IMAGERY

Since the light in the thermal IR range is emitted rather than reflected, there is no need forlight. Thermal emissions from [5] skin are an intrinsic property, independent of illumination.

Hence, the ear images captured by a thermal IR sensor will be invariant to changes in

illumination. So compared to the visible spectrum cameras, the infrared spectrum cameras have

the advantage of better performance under poor light conditions.

Burge and Burger proposed thermal imagery for overcoming the problem of hair occlusion ,

but this is not yet tested. Spoofing of biometrics based on visual imagery, can be, depending on

the system, possible by presenting a high resolution photo to the camera. A thermogram system

cannot be fooled by such an approach. Making a fake that generate a right heat emission pattern

is still not achieved (as far as the author know), and it will obviously be a difficult task because it

requires information of the heat emission of a person. So, thermal imagery used in biometrics

will also have the function as an anti-spoofing technique, providing liveness to the captured data.


Figure 7.3.1 Thermogram Of An Ear


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7.3.2 THERMOREGULATION: FACTORS AFFECTING THE

BODY TEMPERATURE

Thermoregulation is the process of keeping the body at a constant temperature, which

normally is about 37 degrees Celsius. There are certain factors or actions that will make the body

temperature deviate from what is normal.

Such factors are :

Illness

Physical activity

Menstruation

Day rhythm

Environmental temperature

Emotional variations

Food and drink intake

Time of day (related to activity and rest)

Due to these factors, the heat emission image from the body will vary. The body will always

try to keep the inner body temperature constant and reacts differently to hot and cold conditions.

The blood flow is one of the factors that is affected by the thermoregulation mechanisms. When

the body is cold, the blood is routed away from the skin and towards the warmer core of the

body, while when the body is warm the blood is routed towards the skin, thereby increasing heat

loss by radiation and conduction. These changes in the blood flow can also change the thermal

body image and consequently the ear image.



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OTHER DRAWBACKS



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CHAPTER 8

OTHER DRAWBACKS



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Victor et al. compared the performances of PCA when applied on face and ear recognition . In

their experiments, a total of 294 subjects were used. The number of images used in training was

207 for both ears and faces. Different gallery and probe sets were then used for evaluation of

performance. Factors such as time lighting, expression and time lapse between successive image

acquisitions were taken into account. Results showed that in all experiments, face-based

recognition gives better performance than ear-based recognition. A different conclusion was

reached, however, in similar experiments by Chang et al . It was found that there is no significant

difference between the face and the ear in terms of recognition performance. The quality of face

and ear images in the dataset is more rigorously controlled in the experiments reported in.

Images in which the face or ear was substantially obscured by hair or earring were dropped from

the study. Results suggested that the face and the ear might have similar value for biometric

recognition. In one experiment, the respective recognition rates were 70.5 percent and 71.6

percent. Another important finding is that bimodal recognition using both the ear and face results

offers statistically significant improvement over either individual biometric, for example, 90.9

percent in one experiment. The different conclusion reached by the two attempts maybe due to

quality of their data . The image data sets in the study by Victor et al. had less control over the


8.1 Examples of image pairs not used in the study


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variations such as earrings, hair over ears, etc(8.1). However, the results in that study might not

be biased so much if the goal was to reflect the average quality of images likely to be acquired in

real applications. Even though only a small number of images in the previous study exhibited

such quality control issues, these often resulted in misrecognition and, so, excluding them

effectively increases the measured performance for the ear biometric.



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CONCLUSION

CHAPTER 9

CONCLUSION



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The ear as a biometric is no longer in its infancy and it has shown encouraging progress so

far - which is improving, especially with the interest created by the recent research into its 3D

potential. It enjoys forensics support, it's structure appears individual, and it appears to have less

variance with age than other biometrics.

It is also most unusual, even unique, in that it supports not only visual recognition but also

acoustic recognition at the same time. This, together with its deep 3-dimensional structure will

make it very difficult to fake thus ensuring that the ear will occupy a special place in situations

requiring a high degree of protection against impersonation.

The all important question of just how good is the ear as a biometric has only begun to be

answered. The initial test results, even with quite small datasets, were disappointing, but now we

have regular reports of recognition rates in the high 90's on more sizeable datasets. But there is

clearly a need for much better intra-class testing, both in terms of the number of samples per

subject and of variability over time.

Most of the recent work has focused on the overall appearance or on the shape of the ear,

whether it be PCA, force field, or graph model, but it may prove profitable to further investigate

if different and particular parts of the ear are more important than others from a recognition

perspective. There is also a need to develop techniques with better invariance perhaps more

model based, and to seek out high speed recognition techniques to cope with the very large

datasets that are likely to be encountered in practice.

We must not forget that the inherent disadvantage of the occlusion of the ear by hair will

always be a problem, but even this might be ameliorated by the development of thermal imaging

schemes. But one thing is for certain, and that is that there are many questions to be answered, so

we can look forward to many interesting papers addressing these issues.



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GLOSSARY

GLOSSARY



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ATM - Automated Teller Machine

DNA - Deoxy Ribonucleic Acid

CCD - Charge Coupled DeviceFRR - False Reject Rate

ID - Identity

IR - Infrared

LWIR - Long wave Infrared

MWIR - Mid-wave Infrared

PC - Principal Component

PCA - Principal Component Analysis

PIN - Personal Identification number


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REFERENCES

REFERENCES



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[1] EAR BIOMETRICS-Mark Burge and Wilhelm Burger, Johannes Kepler University

Linz, Austria {burge,burger}@cast.uni-linz.ac.at

[2] A tutorial on Principal Components Analysis-Lindsay I Smith February 26, 2002

[3] Biometric Solutions for Personal Identification -Tormod Emsell Larsen

[4] THE EAR AS A BIOMETRIC-D. J. Hurley, B. Arbab-Zavar, and M. S. Nixon

University of Southampton [email protected] [baz05r|msn]@ecs.soton.ac.uk

[5] IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 29, No. 8, August

2007 1297

[6] Recent Advances in Ear Biometrics-K.H. Pun, Y.S. Moon, Department of Computer

Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

[7] http://www.vtk.org, 2006.

[8] A.E. Johnson, http://www-2.cs.cmu.edu/vmr/software/ meshtoolbox, 2004.

[9] M. Choras, Further Developments in Geometrical Algorithms for Ear Biometrics, Proc.

Fourth Intl Conf. Articulated Motion and Deformable Objects, pp. 58-67, 2006.

[10] D. Cremers, Statistical Shape Knowledge in Variational Image Segmentation, PhD

dissertation, Dept. of Math. and Computer Science, Univ. of Mannheim, Germany, July2002.

[11] C. Xu and J. Prince, Snakes, Shapes, and Gradient Vector Flow, IEEE Trans. ImageProcessing, vol. 7, pp. 359-369, 1998.

[12] www.wikipedia.org

[13] Curve Extraction Using Genetic Algorithm Based on Closeness and Continuity in

Perceptive Grouping Factors- Fumihiko Saitoh

[14] EAR BIOMETRICS -Hanna-Kaisa Lammi , Lappeenranta University of Technology,

Department of Information Technology

http://www-2.cs.cmu.edu/vmr/software/http://www-2.cs.cmu.edu/vmr/software/


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