fingerprint detection

BIOMETRIC SYSTEM : FRINGUREPRINT DETECTION

ABSTRACT

“FINGERPRINT RECOGNITION SYSTEM” is a security facility provided to the

users which supports decision to make on the access rights to the authorized

users by authentication.This project extrapolates the necessary fingerprint

data verification and enrollment and requires the users to make decisions

taking high quality image, more responsibility, and accountability and making

comparisons on the ridge patterns of the fingerprints of the users. This

project is based on the fact that each person has a unique pattern of the

fingerprint that differentiates him from others. Fingerprint recognition is a

biometric technique for personal identification. Biometrics based fingerprint

recognition provides one of the promising solutions for the security of the

software and the domain of applying this techniques for security is increasing

day by day. Biometric features also include speech, handwriting, face

identification etc. Face identification is one of the popular techniques for

personal identification, but may fail in certain situations where two people

look very similar. Even the speech and handwriting recognition systems may

fail in certain situations, Fingerprints’ being complex patterns has the

advantage of being a passive, noninvasive system for personal identification

and its success depends on solving the two problems:

Representation of the complex patterns of the fingerprints and

Matching these fingerprint patterns.

This project uses both, algebraic and geometric features to representation

fingerprint images. Here we divide both the existing finger print in the

database and the scanned finger print into frames and compare the pixel

values of the same and the user is authenticated based on the percentage of

values being compared. The constraints of the percentage of fingerprint being

matched can me modified as needed and hence the authentication can be

made as strict as possible based on the criticality of its application.

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TABLE OF CONTENTS

Chapter no TITLE PAGE NO

ABSTRACT

1. INTRODUCTION 5

1.1 BIOMETRIC SYSTEMS 5

1.1.1 What is biometric system? 5

1.1.2 Working of a Biometric System 6

1.1.3 Issues have to be address. 7

1.1.4 The most common biometrics. 8

1.1.5 What is Fingerprint? 12

2. FINGERPRINT DECTION 13

2.1 Finger Print Detection. 13

2.2 PRE-PROCSSING OF IMAGES 13

2.2.1 Binarization 14

2.2.2 THINNING: 14

2.2.2.1 Erosion: 15

2.2.2.2 Dilation: 15

2.2.3 Final Noise Removal 18

2.3 MINUTAE MATCHING

18

2.3.1 CHARACTERSTICS: 18

2.3.2 MINUTAE EXTRACTION 19

2.3.3 FINDING A RIDGE SUMMIT POINT: 19

2.3.3.1 TRACING A RIDGE:

20 2.4 PATTERN MATCHING

21

2.5 FINGERPRINT MATCHING & AUTHENTICATION 22

3 CONCLUSION 24

3.1 ADVANTAGES & DISADVANTAGES 24

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3.1.1 DISADVANTAGES OF USING FINGERPRINT 24

3.1.2 ADVANTAGES OF USING FINGERPRINT 24

3.2 APPLICATIONS 25

4 BIBLIOGRAPHY 26

LIST OF FIGURES

S.NO TITLE PAGE NO

1. Some of the biometrics 5

2. Biometric Market Report in the year 2002. 10

3. fingerprint 11

4. Effect of Binarization 13

5. Figure: Effect of Dilation 14

6. Figure: Effect of Block filter 15

7. Noise removal 15

8. Combined image of both the images 15

9. Crossing over 1 16



12. Figure: Effect of Spurs 17

13. Thinned image from block filtering 17

14. Impact of deleting short island segments 18

15. Figure: Ridge endings 18

16. Figure: Ridge bifurcation 18

17. Figure: Ridg ridges 19

18. Figure: Ridge enclosures 19

19. Figure: minutia attributes 19

20. Figure: RIDGE SUMMIT POINT: 20

21. Figure: Ridge tracing 21

22. Process of identification 23

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Chapter1

INTRODUCTION-

1.1 BIOMETRIC SYSTEMS

1.1.1 What is biometric system ?

A biometric system is essentially a pattern recognition system that

recognizes a person by determining the authenticity of a specific

physiological and/or behavioral characteristic possessed by that person. An

important issue in designing a practical biometric system is to determine

how an individual is recognized. Depending on the application context, a

biometric system may be called either a verification system or an

identification system:·

A verification system authenticates a person’s identity by comparing the

captured biometric characteristic with her own biometric template(s) pre-

stored in the system. It conducts one-to-one comparison to determine

whether the identity claimed by the individual is true. A verification system

either rejects or accepts the submitted claim of identity (Am I whom I claim I

am?);·

An identification system recognizes an individual by searching the entire

template atabasefor a match. It conducts one-to-many comparisons to

establish the identity of the individual. In an identification system, the system

establishes a subject’s identity (or fails if the subject is not enrolled in

thesystem database) without the subject having to claim an identity (Who am

I?).

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Some of the biometrics are: a)ear, b)face, c)facial thermo gram, d)hand

thermo gram, e)hand vein, f)hand geometry, g)fingerprint, h)iris, i)retina,

j)signature, k)voice.

1.1.2 Working of a Biometric System

The term authentication is also frequently used in the biometric field,

sometimes as a synonym for verification; actually, in the information

technology language, authenticating a user means to let the system know the

user identity regardless of the mode (verification or identification). The

enrollment module is responsible for registering individuals in the biometric

system database (system DB). During the enrollment phase, the biometric

characteristic of an individual is first scanned by a biometric reader to

produce a raw digital representation of the characteristic. A quality check is

generally performed to ensure that the acquired sample can be reliably

processed by successive stages. In order to facilitate matching, the raw digital

representation is usually further processed by a feature extractor to generate

a compact but expressive representation, called a template. Depending on the

application, the template may be stored in the central database of the

biometric system or be recorded on a magnetic card or smartcard issued to

the individual.

The verification task is responsible for verifying individuals at the point of

access. During the operation phase, the user’s name or PIN (Personal

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Identification Number) is entered through a keyboard (or a keypad); the

biometric reader captures the characteristic of the individual to be

recognized and converts it to a digital format, which is further processed by

the feature extractor to produce a compact digital representation. The

resulting representation is fed to the feature matcher, which compares it

against the template of a single user (retrieved from the system DB based on

the user’s PIN). In the identification task, no PIN is provided and the system

compares the representation of the input biometric against the templates of

all the users in the system database; the output is either the identity of an

enrolled user or an alert message such as “user not identified.” Because

identification in large databases is computationally expensive, classification

and indexing techniques are often deployed to limit the number of templates

that have to be matched against the input.

1.1.3 When choosing a biometric for an application the following issues

have to be address.

Does the application need verification or identification? If an application

requires an identification of a subject from a large database, it needs a

scalable and relatively more distinctive biometric (e.g., fingerprint, iris, or

DNA).

What are the operational modes of the application? For example, whether

the application is attended (semi-automatic) or unattended (fully

automatic), whether the users are habituated (or willing to be habituated)

to the given biometrics, whether the application is covert or overt,

whether subjects are cooperative or non-cooperative, and so on.

What is the storage requirement of the application? For example, an

application that performs the recognition at a remote server may require

a small template size.

How stringent are the performance requirements? For example, an

application that demands very high accuracy needs a more distinctive

biometric.

What types of biometrics are acceptable to the users? Different biometrics

are acceptable in applications deployed in different demographics

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depending on the cultural, ethical, social, religious, and hygienic standards

of that society.

1.1.4 The most common biometrics.

Ear: It is known that the shape of the ear and the structure of the

cartilaginous tissue of the pinna are distinctive. The features of an ear are

not expected to be unique to an individual. The ear recognition

approaches are based on matching the distance of salient points on the

pinna from a landmark location on the ear.

Face: The face is one of the most acceptable biometrics because it is one

of the most common methods of recognition that humans use in their

visual interactions. In addition, the method of acquiring face images is

nonintrusive. Facial disguise is of concern in unattended recognition

applications. It is very challenging to develop face recognition techniques

that can tolerate the effects of aging, facial expressions, slight variations in

the imaging environment, and variations in the pose of the face with

respect to the camera.

Facial, hand, and hand vein infrared thermograms: The pattern of heat

radiated by the human body is a characteristic of each individual body

and can be captured by an infrared camera in an unobtrusive way much

like a regular (visible spectrum) photograph. The technology could be

used for covert recognition and could distinguish between identical twins.

A thermogrambased system is non-contact and non-invasive but sensing

challenges in uncontrolled environments, where heat-emanating surfaces

in the vicinity of the body, such as, room heaters and vehicle exhaust

pipes, may drastically affect the image acquisition phase. A related

technology using near infrared imaging is used to scan the back of a

clenched fist to determine hand vein structure. Infrared sensors are

prohibitively expensive which a factor inhibiting widespread use of the

thermograms.

Hand and finger geometry: Some features related to a human hand (e.g.,

length of fingers) are relatively invariant and peculiar (although not very

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distinctive) to an individual. The image acquisition system requires

cooperation of the subject and captures frontal and side view images of

the palm flatly placed on a panel with outstretched fingers. The

representational requirements of the hand are very small (nine bytes in

one of the commercially available products), which is an attractive feature

for bandwidth- and memory-limited systems. Due to its limited

distinctiveness, hand geometry-based systems are typically used for

verification and do not scale well for identification applications. Finger

geometry systems (which measure the geometry of only one or two

fingers) may be preferred because of their compact size.

Iris: Visual texture of the human iris is determined by the chaotic

morphogenetic processes during embryonic development and is posited

to be distinctive for each person and each eye. An iris image is typically

captured using a non-contact imaging process. Capturing an iris image

involves cooperation from the user, both to register the image of iris in

the central imaging area and to ensure that the iris is at a predetermined

distance from the focal plane of the camera. The iris recognition

technology is believed to be extremely accurate and fast.

Retinal scan: The retinal vasculature is rich in structure and is supposed

to be a characteristic of each individual and each eye. It is claimed to be

the most secure biometric since it is not easy to change or replicate the

retinal vasculature. The image capture requires a person to peep into an

eyepiece and focus on a specific spot in the visual field so that a

predetermined part of the retinal vasculature may be imaged. The image

acquisition involves cooperation of the subject, entails contact with the

eyepiece, and requires a conscious effort on the part of the user. All these

factors adversely affect public acceptability of retinal biometrics. Retinal

vasculature can reveal some medical conditions (e.g., hypertension),

which is another factor standing in the way of public acceptance of retinal

scan-based biometrics.

Signature: The way a person signs his name is known to be a

characteristic of that individual. Although signatures require contact and

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effort with the writing instrument, they seem to be acceptable in many

government, legal, and commercial transactions as a method of

verification. Signatures are a behavioral biometric that change over a

period of time and are influenced by physical and emotional conditions of

the signatories. Signatures of some people vary a lot: even successive

impressions of their signature are significantly different. Furthermore,

professional forgers can reproduce signatures to fool the unskilled eye.

Voice: Voice capture is unobtrusive and voice print is an acceptable

biometric in almost all societies. Voice may be the only feasible biometric

in applications requiring person recognition over a telephone. Voice is not

expected to be sufficiently distinctive to permit identification of an

individual from a large database of identities. Moreover, a voice signal

available for recognition is typically degraded in quality by the

microphone, communication channel, and digitizer characteristics. Voice

is also affected by a person’s health (e.g., cold), stress, emotions, and so

on. Besides, some people seem to be extraordinarily skilled in mimicking

others.

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Table comparing various biometric technologies High, Medium, and

Low are denoted by H, M, and L, respectively.

Biometric Market Report (International Biometric Group) estimated the

revenue of various biometrics in the year 2002.

1.1.5 What is Fingerprint?

A fingerprint is a textural image containing a large number of ridges that

form groups of almost parallel curves. It has been established that

fingerprint's ridges are individually unique and are unlikely to change during

the whole life.

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Chapter 2

FINGERPRINT DECTION

2.1 Finger Print Detection.

The use of fingerprints as a biometric is both the oldest mode of computer-

aided, personal identification and the most prevalent in use today. However,

this widespread use of fingerprints has been and still is largely for law

enforcement applications. There is expectation that a recent combination of

factors will favor the use of fingerprints for the much larger market of

personal authentication. These factors include: small and inexpensive

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fingerprint capture devices, fast computing hardware, recognition rate and

speed to meet the needs of many applications, the explosive growth of

network and Internet transactions, and the heightened awareness of the need

for ease-of-use as an essential component of reliable security. This method

has been widely used in criminal identification, access authority verification,

financial transferring confirmation, and many other civilian applications. In

the old days, fingerprint recognition was done manually by professional

experts. But this task has become more difficult and time consuming.

2.2 PRE-PROCSSING OF IMAGES

Following image capture to obtain the fingerprint image, image processing is

performed. The ultimate objective of image processing is to achieve the best

image by

which to produce the correct match result.

2.2.1 Binarization

Image binarization is the process of turning a grayscale image to a

black and white image.

In a gray-scale image, a pixel can take on 256 different intensity values

while each pixel is assigned to be either black or white in a black and

white image.

This conversion from gray-scale to black and white is performed by

applying a

threshold value to the image.

A critical component in the binarization process is choosing a correct

value for the threshold. The threshold values used in this study were

selected empirically by trial and error.

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Steps Of Pre-Processing

THINNINGBINARIZATION NOISE REMOVAL


Figure: Effect of Binarization

2.2.2 THINNING:

This thinning method to be done with Block Filtering method attempts to

preserve the outermost pixels along each ridge

This is done with the following steps:

Step One: ridge width reduction

This step involves applying a morphological process to the image to reduce

the width of the ridges. Morphological is a means of changing a stem to adjust

its meaning to fit its syntactic and communicational context Two basic

morphological processes are

Erosion

Dilation

2.2.2.1 Erosion:

Erosion thins objects in a binary image (ridge)In this project we are

using the

2.2.2.2 Dilation:

A dilation process is used to thicken the area of the valleys in the

fingerprint.

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Original Gray Image After

Level Image Dilation

Figure: Effect of Dilation

Step Two: passage of block filter

The next step involves performing a pixel-by pixel scan for black pixels across

the entire image Note that in MATLAB, image rows are numbered in

increasing order beginning with the very top of the image as row one.

Similarly, columns are numbered in increasing order beginning with the

leftmost side of the left to right scan continues until it covers the entire

image. Next, a similar scan is performed across the image from right to left

beginning at the pixel in row one and the last column.

Original image Image after block filter

Figure: Effect of Block filter

Step Three: removal of isolated noise

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Image with noise Image after noise removal

Step Four: scan combination

A value of two means that the pixel from each scan was white, while a value

of zero indicates the pixel from each scan was black. Meanwhile, a value of

one means that the pixel from one scan was black while the same pixel from

the other scan was white. As a result, the new matrix needs to be adjusted to

represent a valid binary image containing only zeros and ones. Specifically,

all zeros and ones are assigned a value of zero (black pixel),

Combined image of both the images

Step Five: elimination of one pixel from two-by-two squares of black

Next, a new scan is conducted on the combined image to detect two-by-two

blocks of black pixels which represent a location where a ridge has not been

thinned to a one-pixel width. It is likely that some of these two-by two blocks

were created by the combination of the previous scans. This problem can be

compensated for by changing one pixel within the block from black to white,

which reduces the width at that particular point from two pixels to one. At

the same time, This operation can be performed by analyzing the pixels

touching each individual black pixel. Note that each black pixel touches the

three other black pixels within the two-by-two block. Therefore, there are

only five other pixels that contain useful information.

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Step Six: removal of unwanted spurs

Crossing over 1

Crossing over 2

Crossing over 3

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With spurs after its removal

Figure: Effect of Spurs

Step Seven: removal of duplicate horizontal and duplicate vertical lines

Thinned image from block filtering

2.2.3 Final Noise Removal

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Impact of deleting short island segments

2.3 MINUTAE MATCHING

2.3.1 CHARACTERSTICS:

A fingerprint is a textural image containing a large number of ridges that

form groups of almost parallel curves. It has been established that

fingerprint's ridges are individually unique and are unlikely to change during

the whole life. Although the structure of ridges in a fingerprint is fairly

complex, it is well known that a fingerprint can be identified by its special

features such as:

Ridge endings: The ending of the ridges takes place at the middle as shown.

Ridge bifurcation : The division of the ridges in the middle as shown

S hort ridges : The small lines present in between two ridges as shown

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Ridge enclosures: These are the loops formed as shown in fig(c).

2.3.2 MINUTAE EXTRACTION

For convenience, we represent a fingerprint image in reverse gray scale. That

is, the dark pixels of the ridges are assigned high values where as the light

pixels of the valleys are given low values. In a fingerprint, each minutia is

represented by its location (x, y) and the local ridge direction Figure 4 shows

the attributes of a fingerprint's minutia. The process of minutiae detection

starts with finding a summit point on a ridge, and then continues by tracing

the ridge until a minutia, which can be either a ridge ending or bifurcation, is

encountered.

2.3.3 FINDING A RIDGE SUMMIT POINT:

To find a summit point on a ridge, we start from a point x = (x1, x2) and

compute the direction angle by using the gradient method. Then theφ

vertical section orthogonal to the direction is constructed. The point in this

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section with maximum gray level is a summit point on the nearest ridge. The

direction angle at a point φ x

mentioned above is computed as follows. A 9×9 neighborhood around x is

used to determine the trend of gray level change. At each pixel u = (u1, u2) in

this neighborhood, a gradient vector v(u) = (v1(u), v2(u)) is obtained by

applying the operator h = (h1, h2) with

to the gray levels in a neighborhood of u. That is,

Where y runs over the eight neighboring pixels around u and g(y) is the gray

level of pixel y in the image. The angle represents the direction of the unit

vector t that is (almost) orthogonal to all gradient vectors v. That is, t is

chosen so that is minimum.

2.3.3.1 TRACING A RIDGE:

The task of tracing a ridgeline to detect minutiae is described in the following

algorithm. This algorithm also constructs a traced image of the fingerprint.

Every time a new summit point of the ridge is found, its location in the traced

image is assigned a high gray value and the surrounding pixels are given

lower gray levels if they have not been marked.

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Algorithm 1 (Ridge tracing):

Start from a summit point x of a ridge.

Repeat

Compute the direction angle at x;

Move pixels from ∝ x along the direction to another point y;

Find the next summit point z on the ridge, which is the local maximum of the

section orthogonal to direction at point y; Set x = z;

Until point x is a termination point (i.e. a minutia or off valid area).

Determine if the termination point x is a valid minutia, if so record it.

End Algorithm 1

2.4 PATTERN MATCHING

The more macroscopic approach to matching is called global pattern

matching or simply pattern matching. In this approach, the flow of ridges is

compared at all locations between a pair of fingerprint images. The ridge flow

constitutes a global pattern of the fingerprint. Three fingerprint patterns are

shown in Figure (Different classification schemes can use up to ten or so

pattern classes, but these three are the basic patterns.) Two other features

are sometimes used for matching: core and delta. (Figure) The core can be

thought of as the center of the fingerprint pattern. The delta is a singular

point from which three patterns deviate. The core and delta locations can be

used as landmark locations by which to orient two fingerprints for

subsequent matching – though these features are not present on all

fingerprints. There may be other features of the fingerprint that are used in

matching. For instance, pores can be resolved by some fingerprint sensors

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and there is a body of work (mainly research at this time) to use the position

of the pores for matching in the same manner that the minutiae are used. Size

of the fingerprint, and average ridge and valley widths can be used for

matching, however these are changeable over time. The positions of scars

and creases can also be used, but are usually not used because they can be

temporary or artificially introduced.

2.5 FINGERPRINT MATCHING AND AUTHENTICATION

Reliably matching fingerprint images is an extremely difficult problem, mainly

due to the large variability in different impressions of the same finger (i.e., large

intra-class variations). The main factors responsible for the intra-class variations

are: displacement, rotation, partial overlap, non-linear distortion, variable

pressure, changing skin condition, noise, and feature extraction errors. Therefore,

fingerprints from the same finger may sometimes look quite different whereas

fingerprints from different fingers may appear quite similar (see Figure 1.14).

Difficulty in fingerprint matching:

Fingerprint look different to an untrained eye but they are impressions of

the same finger.

Fingerprint look similar to an untrained eye but they are from different

fingers.

Human fingerprint examiners, in order to claim that two fingerprints are from the

same finger, evaluate several factors:

i) global pattern configuration agreement, which means that two fingerprints must

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be of the same type,

ii) qualitative concordance, which requires that the corresponding minute details

must be identical,

iii) quantitative factor, which specifies that at least a certain number (a minimum

of 12 according to the forensic guidelines in the United States) of corresponding

minute details must be found, and

iv) corresponding minute details, which must be identically inter-related. In

practice, complex protocols have been defined for fingerprint matching and a

detailed flowchart is available to guide fingerprint examiners in manually

performing fingerprint matching.

Given below is a figure showing the general method by which fingerprints are

matched. Figure showing a general method as to how the finger print is

matched and compared with an existing fingerprint from the database.

Process of identification

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CHAPTER 3

CONCLUSION

3.1 ADVANTAGES & DISADVANTAGES OF USING FINGERPRINT

3.1.1 DISADVANTAGES OF USING FINGERPRINT

There are some problems in collecting the second database:

The different age of the persons leads to a different size of the fingerprint

Some of the twins are children so there are scratches in the fingerprints

Some of them did not fully cooperate with the researchers, so most of the

images of their fingerprints do not contain enough features to create an

extraction.

3.1.2 ADVANTAGES OF USING FINGERPRINT

Prevents unauthorized use or access

Adds a higher level of security to an identification process

Eliminates the burden and bulk of carrying ID cards or remembering Pins

Heightens overall confidence of business processes dependent on personal

identification.

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3.2 APPLICATIONS

Criminal identification

Prison security

ATM

Aviation security

Border crossing controls

Database access

Door-Lock System

Safe Box

Simple Access Controller

Vehicle Control

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BIBLIOGRAPHY

i. www.biometix.com

ii. www.biomet.org

iii. www.owlinvestigation.com

iv. www.ddl.ision.co.uk

v. www.zdnetindia.com/techzone/resources

vi. www.biodata.com.au

vii. http://en.wikipedia.org/wiki/1-Wire

viii. http://fingereprint.blogspot.com/

ix. Baruch,O.Following", Pattern Recognition Letters, Vol. 8 No. 4, 1988, pp.

271-276.

x. Nist image group’s fingerprint research.

http://www.itl.nist.gov/iad/894.03/fing/fing.html. [Online; accessed

25-February-2010].

xi. Fvc2006 the fourth international fingerprint verification competition.

http://bias.csr.unibo.it/fvc2006/results/O res db2 a.asp. [Online; accessed

25-February-2010].

xii. Fvc2004 the third international fingerprint verification competition.

http://bias.csr.unibo.it/fvc2004/results.asp. [Online; accessed 25-

February-2010].

xiii. S.A. Niyogi and E.H. Adelson. Analyzing and recognizing walking

figures in xyt. CVPR, 94:469–474.

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http://fingereprint.blogspot.com/

fingerprint detection

Technology

identification system

practical biometric

verification system

noninvasive system

biometric systems

pattern recognition

biometric features

biometric technique