An Overview of Biometric Image

Processing

CHAPTER 2

AN OVERVIEW OF BIOMETRIC IMAGE PROCESSING

The recognition of persons on the basis of biometric features is an emerging

phenomenon in our society. Traditional systems to verify a person’s identity are based on

knowledge (secret code) or possession (ID card). However, codes can be forgotten or

overheard, and ID cards can be lost or stolen, giving impostors the possibility to extend

the identity test. These existing issues have received increasing identity in recent years

about the use of features inseparable from a person’s body which significantly decreases

the possibility of a hoax. The need for security in a wide scope of applications, such as

replacement of the Personal Identification Number (PIN) in banking and retail business,

security of transactions across computer networks, high-secure wireless access,

tele-voting, and admittance to restricted areas can be practiced with the help of biometric

based authentication. This chapter discusses about the overview of digital image

processing, about biometric system and the importance of biometric technology in

real-time applications.

2.1 Digital Image Processing

The process of obtaining and analyzing visual data from digital data processor is

called Digital Image Processing and Scene Analysis. Digital Image Processing [10] is a

rapidly developing subject area with growing applications in science and technology.

Image Processing has the likelihood of acquiring the ultimate machine that could execute

the optical functions of all existing organisms. Many theoretical and technological

discoveries are required before it could make such a machine that is there is an

abundance of Image Processing applications that can help humanity with the availability

and anticipated technology in the near future. Imaging began in the 19th century with

photography and continued with television, X-rays and electronic scanning during the

20th century. Image Processing arose as a field of study in the 1950s with pictures of the

solid ground from high-flying spy airplanes and then with pictures of the earth’s surface

taken from orbiting satellites.

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Processing of an image includes improvement in its appearance and efficient

representation. This discipline consists of not just feature extraction, analysis and

recognition of images, but also coding, filtering, enhancement and restoration. The entire

process of image processing and analysis starts from receiving of visual information to

the giving out of the description about the scene. It can be divided into three main stages

[16] as given below:

• Discretization and Representation: Translating visual information into a

discrete form suitable for computer processing, approximating visual information

to save storage space as well as time requirements in subsequent processing.

• Processing: Improving image quality by filtering, etc., reducing data to save

storage space and channel capacity during transmission.

• Analysis: Extracting image features; quantifying shapes, registration and recognition.

2.2 Outline of Biometric Technology

The emerging field of biometric technology addresses the automated

identification of individuals, depending on their physiological and behavioral traits.

The broad category of human authentication schemes, denoted as biometrics encompasses

many techniques from Computer Vision and Pattern Recognition. The personal attributes

that are used in a biometric identification system can be physiological or behavioral. The

physiological biometric identification system includes fingerprints, palm print, facial

features, iris, retinal scans, and hand and finger geometry. The behavioral biometric

identification system considers the character idiosyncratic of the individual, such as gait,

voice print, sign and key stroking. Depending on the complexity or the security level of

the application, one will opt to use one or more of these personal characteristics [11].

Biometric systems have been actively emerging in many applications of diverse

industries for the past few years and it is continuing to roll out to provide higher security

features to access control system. Many cases of single modal biometric systems have

been built up and deployed, for example, fingerprint, face, speaker, palm print and hand

geometry verification systems. These systems are capable of providing a low to middle

range of security features. For higher security features, the blend of two or more single-

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modal biometrics (also known as multimodal biometrics) is also proposed. Biometrics are

the most secured and convenient method to satisfy the need for identifying the

individuals in the society. For automatic identification of an individual, physiological or

behavioral characteristics of a person are used [11].

2.3 Prerequisites of a Good Biometric

Many different traits of human physiology, behavior or chemistry can be used for

biometric authentication. The choice of an individual biometric for use in a specific

application involves weighing of various components. Jain et al [12] acknowledged seven

such factors to be employed when evaluating the suitability of any trait for use in

biometric authentication.

• Universality means that each person using a system should hold the quality.

• Uniqueness means that the quality should be sufficiently unique for individuals in

the relevant population, such that they can be notable from each other.

• Permanence associates the manner in which a quality varies over time. More

specifically, a trait with 'good' permanence will be reasonably invariant over time

with respect to the specific matching algorithm.

• Measurability (collectability) relates to the ease of acquisition or measurement of

the trait. Additionally, acquired data must be in a form that permits subsequent

processing and extraction of the related feature sets.

• Performance relates to the speed, robustness and accuracy of technology used.

• Acceptability relates to how good the individuals in the relevant population

accept the technology such that they are willing to have their biometric trait

captured and measured.

• Circumvention relates to the efforts with which a trait might be imitated using an

artifact or substitute.

2.4 Need for Biometrics

In the current times, most of the transactions either finance or other secure

messages are automated and many of them networked, so security has gone forth a most

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important subject. Security is commonly in the form of belongings like ID cards, keys or

secret knowledge like password. This type of security is not guaranteed as for example,

ID cards may be lost, and passwords may be forgotten or compromised. A sturdy need

was therefore felt for more robust authentication methods and far-reaching research

ensued in this area. This led to the concept of using human body parts or human

mannerism itself as security and authentication measure, and eventually to the emergence

of biometrics as a subject by itself. Nowadays it is widely recognized that any positive

recognition of a person must take into account of his biometric identification.

2.5 Biometric System as Pattern Recognition

A biometric system is basically a pattern recognition system that works by

acquiring biometric data from an individual, drawing out a feature set of the acquired

data and then comparing this feature set against the template set in the database.

Depending on the context of application, a biometric system could function either in

identification mode or verification mode. The block diagram depicts the two fundamental

modes of a biometric system [13] as exposed in figure 2.1

Figure 2.1 Block Diagram of the General Biometric Detection System [14]

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First block, known as sensor acts as an interface between system and the real

world and acquires necessary data. Vision based picture acquisition system is convenient

choice for it, but can be changed as per application. Second block performs pre-processing,

i.e. removes artifacts from the sensor and enhances the input picture. In the next block,

essential characteristics are extracted. This is a critical stage where the right features are

required to be extracted in an optimal way. Image features or vector of the numbers with

specific properties is used for the formation of the template. A template is a

combinational set of related features extracted from the source. Elements of biometric

measurements those are not required for comparison algorithms are banished in templates

for reducing size of file and for protecting the individuality of the claimer.

2.6 Modes of Biometric System

2.6.1 Verification or Authentication Mode

The system performs a one-to-one comparison of a captured biometric with a

specific template stored in a biometric database in order to verify the individual is the

person they claim to be. Three steps are involved in the verification of a person [13].

Step 1: Reference models for all the users are generated and stored in the model

database.

Step 2: With samples that are matched with reference models to generate the

genuine and impostor scores and the threshold is calculated.

Step 3: In this testing, the process was performed which may use a username,

smart card or ID number (e.g. PIN) to indicate which template should be

used for comparison.

2.6.2 Identification Mode

The system performs a one-to-many comparison against a biometric data base in

an attempt to establish the identity of an unknown individual. The system will succeed in

identifying the individual if the comparison of the biometric sample to a template in

the database falls within the previously set threshold. The identification mode may be

used either for 'positive recognition’ or for 'negative recognition' of the person. The latter

recognition will only be achieved through biometrics since other methods of personal

recognition such as PINs, passwords or keys are ineffective [11].

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During the enrollment phase, the template is simply stored somewhere (on a card

or within a database or both). During the matching phase, the template obtained is passed

to a matcher that compares it with other available templates, approximating the distance

between them using any algorithm (e.g. Hamming distance). The matching program will

analyze the input with the template. This will subsequently be an output for any specified

use or purpose (e.g. Entrance in a restricted area). Selection of biometrics in any practical

application depends upon the characteristic measurements and user requirements [13] .

Selecting a biometric based on the user requirement takes into account of the availability of

sensor devices, computational time, reliability, sensor area, cost and power consumption.

2.7 Phases of Biometric System

A biometric system is designed using the following three main phases [15] as

shown in Figure 2.2.

Figure 2.2 Modules of a Biometric System [16]

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2.7.1 Biometric Image Capturing Phase

In this phase, the raw biometric is captured by a sensing device such as a

fingerprint scanner or video camera. An example of such a device is a fingerprint sensor

that captures images like the ridge and valley structure of a user’s finger.

2.7.2 Feature Extraction Phase

The second phase of processing is to extract the distinguishing characteristics of the

raw biometric sample and convert it into a processed biometric identifier record, in which the

acquired biometric data are processed to extract the salient or discriminatory features.

2.7.3 Matcher Phase

This phase works during the authentication process in which the features

extracted during recognition are compared against the stored templates to generate

matching scores. The matcher module also encapsulates a decision-making model, where

the identity of a claimed user is confirmed (verification) or a user’s identity is established

(identification) based on the matching score.

2.7.4 Template Database Phase

This phase is used by the biometric system to store the biometric templates of the

registered users. To store the biometric features or templates of enrolled users, the

enrollment module holds the responsibility for enrolling individuals in the biometric

system database. During the phase of enrollment, the biometric characteristic of an

individual is scanned first by a biometric reader to produce a digital representation of the

characteristic. The data that is captured during the enrollment process may or may not be

supervised by a human depending on the application. A quality check is generally

performed to ensure that the acquired sample can be reliably processed by succeeding

stages. In order to help the process of matching, the input digital representation is further

treated by a feature extractor to generate a compact but expressive representation, called a

template. Based on the application, the template can be stored in the central database of

the biometric system or be recorded on a smart card issued to the individual person.

Usually, several templates of an individual are stored to account for variations observed

in the biometric trait and the templates in the database may be updated over time.

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2.8 Uses of Biometrics

The uses of Biometric system are listed below

• Firstly, biometric systems can be used as physical access granting systems.

• Secondly, biometric systems can be used to establish entitlement to services and

rights.

• Thirdly, biometric systems can be used for the recording and association of facts.

2.9 Types of Biometrics

The Biometrics system falls into two categories, they are physical biometrics and

behavioral biometrics, which are explained in this section.

2.9.1 Physical Biometrics

Physiological traits are related to the shape of the body. Examples include, but are

not limited to fingerprints, face recognition, DNA, hand and palm geometry, iris

recognition, which has largely replaced retina, and odor/scent [17]. Physical biometrics

evaluates certain unique physical characteristics of a person’s body. Types of physical

biometric devices are:

• Fingerprint Scanners

• Hand Geometry Scanners

• Iris Scanners

• Retinal Scanners

• Facial Scanners

Fingerprint Scanners

Fingerprint scanners are devices that scan an individual’s fingerprint and compare

it to a pre-existing fingerprint template [16]. This is most commonly used biometric

device. The advantages of fingerprint scanners are inexpensive and it has the ability to

enroll multiple fingerprints. The disadvantage of fingerprint scanners is it’s vulnerable to

dirt and its’ association with criminality.

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Figure 2.3 Fingerprint Scanner

Hand Geometry Scanners

Hand Geometry scanners are devices which measure an individual’s hand based

on their hand’s size and shape [18]. The advantage of hand geometry scanners is its

simple to use and it works quickly and easily. The disadvantages are its limited accuracy

and the machine is large and bulky.

Figure 2.4 Hand Geometry Scanners

Iris Scanners

Iris scanners analyze the pattern of color surrounding one’s pupils [17].

The advantage of Iris scanners is its simple cameras and doesn’t require close contact and

results are accurate. The disadvantages are its being expensive and not easy to use.

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Figure 2.5 Iris Scanners

Retinal Scanners

Retinal scanners analyze patterns of blood vessels in the back of one’s eye [19].

The advantage of retinal scanners is its very accurate and the patterns don’t change often.

The disadvantages are its difficult to use, expensive, large in size and require close contact.

Figure 2.6 Retinal Scanners

Facial Scanners

Facial recognition starts by using a digital video camera to record a person’s face

as they enter a certain area. This type of biometrics does not require anyone to physically

touch a machine, just stand within a designated space. Facial Scanners analyze one’s

facial characteristics. The advantage of facial scanners, it uses normal cameras and does

not require cooperation of others. The disadvantages are it needs adequate lighting and

faces change over time.

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Figure 2.7 Facial Scanners

2.9.2 Behavioral Biometrics

Behavioral biometrics is related to the behavior of a person. Examples include, but

are not limited to typing rhythm, gait, and voice. Some researchers have coined the term

behaviometrics for this class of biometrics. Behavioral biometric devices analyze

particular behavioral characteristics of an individual. Types of behavioral biometric

devices [20] are,

• Signature verification scanners

• Voice authentication scanners

• Keystroke scanners

Signature Verification

Signature Verification Devices analyze a person’s signature based on shape of

signature, speed and pressure. The key advantage of this particular system of behavioral

biometrics is that it was based on an already accepted form of identification.

Incorporation of a security system based on Dynamic Signature Verification would

require a certain amount of investment in equipment and software to analyze the inputs,

but no real cost to train people on how to input signals. The disadvantages are high error

rates. The process is ideal for security purposes because it allows a frequently used

writing (the signature) that is unique to each user based upon the amount of time and

effort that they specifically put into their writing.

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Figure 2.8 Signature Verification Scanners

Voice Authentication

Voice authentication devices analyze an individual’s voice and transform their

words into text, which is often referred to as voice to print technology. The voiceprint is a

biometric voice identifier and not a recording or a sound file; so an imposter could not

record one’s words and replay them into the system and get access granted. A voiceprint

allows the user to gain access to information or give authorization without being

physically present; this way the user can give authorization by way of a simple phone call.

The advantage is it’s easy to use. Its disadvantage is background noise and voice changes.

Figure 2.9 Voice Authentication Scanners

Keystroke Scanners

Keystroke scanners are software programs that evaluate a person’s typing patterns

based on “flight time” and “dwell time”. One of the most likely possible uses for

keystroke dynamics in the business and information world today would be for user

identification purposes. Once proper calibrated, the template will be easily able to

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distinguish whether the acceptable user is typing or not by comparing the flight and dwell

times to those set in the template. The advantage is it’s easy to use between software

versus physical device. The disadvantage is its accuracy is very limited.

Figure 2.10 Keystroke Biometric Authentication Scanners

2.10 Performance Evaluation

Referable to the variations existing within any biometric signal, a biometric

authentication or recognition system cannot render an absolute answer about the

individual's identity; rather it provides the individual's identity information with a

certain confidence level. This is adverse to traditional authentication systems (for

instance, a password system) where the match has to be exact and an absolute “yes” or

“no” response is given. The biometric signal variations of an individual are usually

referred to as intra-class variations (Figure 2.11); whereas variations between different

individuals are called inter-class variations.

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Figure 2.11 : Intra-class variation.

Figure 2.11 shows the examples of intra-class variation. There are eight different

fingerprint impressions of the same finger. Note that huge differences of image

contrasts, locations, rotations, sizes, and qualities, exist among them.

A biometric matcher takes two biometric signals, and returns a similarity

measurement result. If the result becomes closer to 1, the matcher recognizes more

confidently that both biometric signals come from the same individual; when it becomes

closer to 0, the matcher recognizes that both biometric signals come from the same

individual with lesser confidence. Generally, the identity of a submitted biometric signal

is either a genuine type or an impostor type; hence, there are two statistical distributions

of similarity scores, which are called genuine distribution and impostor distribution

(Figure 2.12). Each type of input identity has one of the two possible results, “accept” or

“reject”, from a biometric matcher.

Consequently, there are four possible outcomes:

1. A genuine individual is accepted;

2. A genuine individual is rejected;

3. An impostor individual is accepted;

4. An impostor individual is rejected.

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The first and fourth outcomes are correct while the second and third outcomes

represent the error situations. The second outcome is referred to as “false reject” and the

corresponding error rate is called False Reject Rate (FRR); the third outcome is referred

to as “false accept” and the corresponding error rate is called False Accept Rate (FAR).

They are the most widely used measurements in today's commercial environment. Given

a genuine distribution, pg , and impostor distribution, pi, the FAR and FRR at threshold

‘th’ is given by

𝐹𝐹𝐹𝐹𝐹𝐹(𝑇𝑇) = ∫ 𝑝𝑝𝑖𝑖(𝑥𝑥)𝑑𝑑𝑥𝑥 1𝑡𝑡ℎ 2.1

𝐹𝐹𝐹𝐹𝐹𝐹(𝑇𝑇) = ∫ 𝑝𝑝𝑔𝑔(𝑥𝑥)𝑑𝑑𝑥𝑥 𝑡𝑡ℎ0 2.2

Strict tradeoff exists between FAR and FRR in every biometric system [21].

From equation 2.1 and 2.2, both FAR and FRR are actually functions of threshold ‘th’.

When ‘th’ decreases, the system would have more tolerance to intra-class variations and

noise, however the FAR will increase. Similarly, if the value of ‘th’ is lower, the system

would be more secure and the FRR decreases. The following figure 2.12 shows the

example of genuine and imposter distributions. The red line is the imposter and the blue

line is the genuine.

Figure 2.12: Example of Genuine and Impostor Distributions

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Depending on the nature of an application, the biometric system may be chosen to

operate at low FAR configuration (for example, the login process in ATMs), or to operate

at low FRR configuration (for example, the access control system for a library). A system

designer may have no prior knowledge about the nature of the application in which the

biometric system is to be applied, thus it is helpful to report the system performance at all

possible operating points (thresholds).

Other useful performance measurements are:

• Equal Error Rate (EER): the error rate where FAR equals to FRR.

• ZeroFNMR: the lowest FAR at which no false reject occurs.

• ZeroFMR: the lowest FRR at which no false accept occurs.

• Failure To Capture Rate: the rate at which the biometric acquisition device fails

to automatically capture the biometric signals. A high failure to capture rate

makes the biometric system hard to use.

• Failure To Enroll Rate: the rate at which users are not able to enroll in the

system. This error mainly occurs when the biometric signal is rejected due to its

poor quality.

• Failure to Match Rate: occurs when the biometric system fails to convert the

input biometric signal into a machine readable/understandable biometric template.

Unlike FRR, a failure to match the error occurs at a stage prior to the decision

making stage in a biometric system.

2.11 Merits and Demerits of Biometric System

The merits of the biometric system are as follows,

• It is significantly more difficult to copy, share, and distribute biometrics with as

much ease as passwords and tokens.

• Biometrics cannot be lost or forgotten and online biometrics-based recognition

systems require the person to be recognized to be present at the point of

recognition.

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• It is difficult to forge biometrics and extremely unlikely for a user to repudiate,

for example, having accessed a computer network.

• Further, all the users of the system have a relatively equal security level and one

account is no easier to break than any other (e.g., through social engineering methods).

• Biometrics introduce incredible convenience for the users (as users are no longer

required to remember multiple, long and complex, frequently changing passwords)

while maintaining a sufficiently high degree of security.

Biometric systems that operate using any single biometric characteristic have few

issues to be monitored are as follows:

• Noise in sensed data: The sensed data might be noisy or distorted

• Intra-class variations: The biometric data acquired from an individual during

authentication may be very different from the data that was used to generate the

template during enrollment, thereby affecting the matching process.

• Distinctiveness: While a biometric trait is expected to vary significantly across

individuals, there may be large inter-class similarities in the feature sets used to

represent these traits.

• Non-universality: While every user is expected to possess the biometric trait

being acquired, in reality it is possible for a subset of the users to not possess a

particular biometric.

• Spoof attacks: An impostor may attempt to spoof the biometric trait of a

legitimate enrolled user in order to circumvent the system. This type of attack is

especially relevant when behavioral traits such as signature and voice are used.

2.12 Summary

The successful installation of biometric systems in various civilian applications

does not imply those biometrics are a fully solved problem. It is clear that there is plenty of

scope for improvement in biometrics. Researchers are not only addressing issues related to

reducing error rates, but they are also looking at ways to enhance the usability of biometric

systems. Biometric systems that operate using any single biometric characteristic have some

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limitations which will lead to poor identification results. This chapter provides the

general idea of biometric system functionalities and its applications. The proposed

research work concentrates on fingerprint based image processing. The next chapter

discusses about an elaborate study on fingerprint recognition system.

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