v. fingerprints - shodhgangashodhganga.inflibnet.ac.in/bitstream/10603/99462/12/12...possesses...

Fingerprint as Biometric: Feature, Application and Recognition

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V. Fingerprints

Fingerprint recognition is a popular, very widely used and accurate Biometric technology.

Nowadays, fingerprints are being used in many real life applications such as access control,

forensics, etc.

Among all the biometric techniques, fingerprint-based identification is the oldest method

which has been successfully used in numerous applications. Everyone is known to have

unique, inimitable fingerprints. Fingerprints are formed even before a person takes birth and

doesn’t change throughout the lifespan of an individual.

Fingerprints are not an entirely genetic characteristic, thus even identical twins have different

fingerprints. The ultimate shape of fingerprints are believed to be influenced by

environmental factors during pregnancy, like nutrition, blood pressure, position in the womb

and the growth rate of the fingers at the end of the first trimester.

V.1. Physical Characteristics

A fingerprint in its narrow sense is an impression left by the friction ridges of a human finger.

In a wider use of the term, fingerprints are the traces of an impression from the friction ridges

of any part of a human or other primate hand. A print from the foot can also leave an

impression of friction ridges.

Human fingertips contain ridges and valleys which together form distinctive patterns. These

patterns are fully developed under pregnancy and are permanent throughout whole lifetime.

Prints of those patterns are called fingerprints. Injuries like cuts, burns and bruises can

temporarily damage quality of fingerprints but when fully healed, patterns will be restored.

Figure 25: Sample fingerprint images


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Fingerprints have long been used for personal identification. It is assumed that every person

possesses unique fingerprints [121] and hence the fingerprint matching is considered as one

of the most reliable techniques of people identification.

A fingerprint image exhibits a quasiperiodic pattern of ridges (darker regions) and valleys

(lighter regions). The local topological structures of this pattern together with their spatial

relationships determine the uniqueness of a fingerprint. There are more than 100 different

types of local ridge structures that have been identified [121]. Nevertheless, most of the

automatic fingerprint identification / verification systems adopt the model used by the Federal

Bureau of Investigation [17].

Figure 26: Fingerprint Types

Figure 25 shows sample fingerprints and figure 26 shows different fingerprint types.

The ridges on fingers are created during embryo development in response to pressures that

form patterns that can be classified by print examiners. These ridges are also referred to as


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friction ridges. They provide a relatively rough surface area, making it possible to grasp and

hold on to objects with ease. Each ridge contains at least one pore, which is connected to a

sweat gland below the skin. The sweat gland helps to remove waste from the ridge area as

well as to maintain a relatively constant temperature through evaporation. The sweat

produced is also the source of deposits for latent prints, i.e., those finger images that remain

on a surface after it has been touched. In addition to water, the sweat contains trace elements

of oil and some minerals. These latent impressions remain on the contact surface after it is

touched. The condition of the surface, e.g., if it is shiny or porous, affects how much of the

sweat remains on the surface. Environmental factors such as heat and humidity also influence

how long the latent print will remain. Latent prints are left by everyone on almost every kind

of surface. Virtually anytime an object is touched by the body, it retains some of the body’s sweat. In the case of finger and palm images, these can form a unique combination of ridges,

ridge endings, bifurcations, core and delta locations, and other image characteristics. For a

simple exhibit of a latent print, let’s take a clear, colourless glass and wipe the exterior

surface to remove any foreign material. Let’s put some water into the glass; next, hold the

glass of water and take a drink; put the glass down and release it from hand; and now look at

the glass. The images that are seen on the glass are latent prints. Fingerprints have been

compared to topographical maps. The contour lines of the maps are similar to the friction

ridges of fingerprints, which consist of ridge endings, bifurcations, and dots. They generate a

flow that can be identified as a pattern. They do not appreciably change over time. Unlike

contour lines, however, friction ridges remain relatively uniform in their spatial distances and

are rarely featureless. In contrast, the contour lines on a topographical map appear more

closely together to indicate a sharp change in elevation, and relatively large spaces between

lines indicate that the surface has only a gradual slope.

The finger images shown in Figure 25 are representative of the millions of fingerprint images

on file. The image contains a great amount of information that contributes to the uniqueness

of the fingerprint image, particularly to someone trained to look for it. For example, the

friction ridges flow around a centre area. If this were a topographical map, it would be

interpreted as a mountain or hill. The point at which the ridges form would be the top of that

hill. The change in elevation is constant and the ridges are uniformly distant from each other.

The white areas are creases or scars. The introduction of scars does not negate the value of

the fingerprint image. In some instances, it might even aid in the identification, depending on

the size and location of the scar.

We have already discussed about the topic “Why Fingerprint?”

V.2. Proven Uniqueness Can it be proved that no two finger images are the same? To do that would require that every

fingerprint be collected and compared. Each of the more than seven billion persons on this

planet, most of whom have ten fingers, would have to be fingerprinted. Those prints would

build a database of approximately seventy billion images, which would then have to be


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searched by the ten fingers of each of the 7000000000 people. In AFIS (Automatic

Fingerprint Identification Systems) parlance, this is called a self-search, i.e., a portion of the

database is searched against the rest of the database until the entire database has been

searched against itself.

There are instances in which a person has been fingerprinted more than once and the

identification is missed on a subsequent search, perhaps because of a poor-quality set of

images on the database, poor-quality inked impressions, or inaccurate data entry. When these

records are found, the images are reviewed by a print examiner. If the different records are of

the same individual, the records are consolidated into one record. There are also instances in

which identification is made on an individual whose finger images match the inquiry card,

but the name (or some other characteristic) does not match. What is the true name of the

subject? The identification agency can only report on the information it has on file. Large

identification agencies may initiate a self-search of the database once the system is fully

operational or when major improvements to the system have occurred, such as the installation

of more accurate matchers (which house extracted image characteristics) that will match

minutiae with a higher level of precision, or improvements to the coders (which extract

features from finger images) that will more accurately find the minutiae in the finger image.

For example, a few years ago a large identification agency installed new coders and new

matchers, and systematically began to undertake a self-search. In searching the two index

fingers of these five million records, the agency uncovered hundreds of records that had to be

consolidated, some with more than ten entries for the same person. In all of those searches,

however, there was not one instance in which two different persons had identical finger

images. Five million records may be much fewer than seventy billion, but it is a good

representative sample and a good test. So to the question “Can it be scientifically proved?” the response is “Not in the immediate future.” But there are other indicators. AFIS systems

have been around for over few decades. Their matchers have compared millions of finger

images.

V.3. Features The fingerprint is a duplicate of a fingertip epidermis; when a person touches a smooth

surface, the fingertip epidermis characteristic is transferred to the surface. The pattern of the

ridges and valleys on the human fingertips forms the fingerprint images.

Analyzing this pattern at different levels reveals different types of features that are, global

feature and local feature.

Global features shape a special pattern of ridge and valleys, called singularities or Singular

Point (SP) and the important points are the core and the delta. The core (shown as O) is

defined as the top most point on the in the centre of inner most ridges and a delta (shown as

Δ) is defined as the centre point where three different directions flows meet. The SP provides


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important information for fingerprint classification, fingerprint matching and fingerprint

alignment.

Local features or the so-called minutiae are an important feature for fingerprint matching.

Figure 27: Fingerprint image showing different ridge features

(a) (b) (c)

Figure 28: Minutiae

(a) Fingerprint image showing Minutiae,

(b)Minutiae,

(c) Types of false minutiae structures: from left to right and up to bottom we have: spike, bridge, hole,

break, spur, and ladder structure. The false minutiae generated by each structure are marked as (x) false

ridge ending, and (o) false ridge bifurcation.

Fingerprint patterns are full of ridges and valleys. The information of the ridge structures can

be treated as three levels. At the coarse level, the number and the relative positions of


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singular points, including cores and deltas, are concerned for classification. At the fine level,

the minutiae, a group of ridge endings and bifurcations, are used as the features for matching.

Between the above two levels, the middle level also contains important information,

including local ridge orientation (LRO) and local ridge frequency (LRF). Conventionally,

only the structures of LROs are used to find the singular points for classification or to

enhance ridge structures for minutiae extraction.

Although other approaches are possible, like, for instance, the hashing technique in the

minutiae domain, the first step in an identification system is often continuous classification of

fingerprints. This reduces the partition of the database to be searched for matches. To

facilitate high-performance classification, algorithms for accurate singular-point estimation

are needed. Singular point detection is a critical process for both fingerprint matching and

fingerprint classification. The process of singular points detection must be fast and robust;

otherwise, the performance of the whole fingerprint recognition system would be influenced

heavily.

In high level fingerprint classification algorithms, extracting the number and precise location

of singular points (SP), namely core and delta points are of great importance. According to

the number and location of these robust characteristics, fingerprints can be classified in to

main groups; arch, tented arch, right loop, left loop, whorl, etc.

Using high-level classification process can efficiently reduces the search area in large

fingerprint databases and therefore speeds up the subsequent matching algorithm. There are

four main approaches to allocate SPs [129].

• Methods based on mathematical model representation of fingerprint: Because of the

complexity of fingerprint patterns, representation of an accurate model for these images is a

difficult task that cannot be achieved always by this type of models [24], [145].

• Methods based on statistical approaches: In these methods although the usage of histogram

has reduced the noise effect, however they cannot adapt themselves to different image

characteristics [38].

• Methods based on different frequency transforms like Fourier Transform, Gabor Transform:

These methods are not efficient enough because of working in the frequency domain. But

some have claimed to obtain fair results [59].

• Methods based on fingerprint structures: These are usually well applied approaches, which

have been tested successfully on large databases [35], [60], [61].

Some approaches combine several types of the above mentioned methods and make a new

combined system [62].

Singular points detection is the most challenging and important process in biometrics

fingerprint verification and identification systems. Singular points are used for fingerprint

classification, fingerprint matching and fingerprint alignment.


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Figure 29: Examples of a fingerprint, its directional field and its singular points:

(a) fingerprint, (b) directional field, and (c) singular points

Figure 30: Segments of a fingerprint that contain a singular point. (a) Core and (b) delta.


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Nowadays, most Automatic Fingerprint Identification Systems (AFIS) are based on matching

minutiae, which are local ridge characteristics in the fingerprint pattern.

The two most prominent minutiae types are ridge ending and ridge bifurcation.

Based on the features that the matching algorithms use, fingerprint matching can be classified

into image-based and graph-based matching.

Image-based matching [122] uses the entire gray scale fingerprint image as a template to

match against input fingerprint images. The primary shortcoming of this method is that

matching may be seriously affected by some factors such as contrast variation, image quality

variation, and distortion, which are inherent properties of fingerprint images. The reason for

such limitation lies in the fact that gray scale values of a fingerprint image are not stable

features.

Graph-based matching [2], [63] represents the minutiae in the form of graphs. The high

computational complexity of graph matching hinders its implementation. To reduce the

computational complexity, matching the minutiae sets of template and input fingerprint

images can be done with point pattern matching. Several point pattern matching algorithms

have been proposed and commented in the literature [3], [4], [67], [123], [127].

Fingerprint system can be separated into two categories Verification and Identification.

Verification system authenticates a person’s identity by comparing the captured biometric characteristic with its 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.

Identification system recognizes an individual by searching the entire template database for

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 the system database) often without the subject having to claim an identity.

Acceptance rate accuracy is often measured in terms of false acceptance rate and false

rejection rate [68].

False acceptance rate or false match rate (FAR or FMR): the probability that the system

incorrectly matches the input pattern to a non-matching template in the database. It measures

the percent of invalid inputs which are incorrectly accepted. In case of similarity scale, if the

person is an imposter in reality, but the matching score is higher than the threshold, then he is

treated as genuine. This increases the FAR, which thus also depends upon the threshold

value.

False rejection rate or false non-match rate (FRR or FNMR): the probability that the

system fails to detect a match between the input pattern and a matching template in the

database. It measures the percent of valid inputs which are incorrectly rejected.


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In order to implement a successful algorithm, it is necessary to understand the topology of a

fingerprint. A fingerprint consists of many ridges and valleys that run next to each other,

ridges are shown in black and valleys are shown in white. The ridges bend in such ways as to

form both local and global structures; either of which can be used to identify the fingerprint.

The global level structures consist of many ridges that form arches, loops, whorls and other

more detailed classifications, as shown in Figure 26. Global features shape a special pattern

of ridge and valleys. On the other hand, the local level structures, called minutiae, are further

classified as either endpoints or bifurcations. Minutiae are also given an associated position

and direction, as shown in Figure 31 and in Figure 33. Other than usual minutiae there are

sweat pores in the fingerprint which can also be used for fingerprint matching.

Figure 31: Types of fingerprint minutiae and their respective directions: (a) an endpoint, (b) a bifurcation.

One of the most popular biometric traits, fingerprints are widely used in personal

authentication, particularly with the availability of a variety of fingerprint acquisition devices

and the advent of thousands of advanced fingerprint recognition algorithms. Such algorithms

make use of distinctive fingerprint features that can usually be classified at three levels of

detail [73], as shown in Figure 35 and referred to as level 1, level 2, and level 3.

Level-1 features are the macro details of fingerprints, such as singular points and global ridge

patterns, e.g., deltas and cores (indicated by triangles in Fig. 35). They are not very

distinctive and are thus mainly used for fingerprint classification rather than recognition.

The level-2 features (rectangles) primarily refer to the Galton features or minutiae, namely,

ridge endings and bifurcations. Level-2 features are the most distinctive and stable features,

which are used in almost all Automated Fingerprint Recognition Systems (AFRS) [69], [73],

[79], [80] and can reliably be extracted from low-resolution fingerprint images (~500 dpi). A

resolution of 500 dpi is also the standard fingerprint resolution of the FBI for AFRSs using

minutiae [80].

Level-3 features (circles) are often defined as the dimensional attributes of the ridges and

include sweat pores, ridge contours, and ridge edge features, all of which provide quantitative

data supporting more accurate and robust fingerprint recognition. Among these features,

pores have most extensively been studied and are considered to be reliably available only at a

resolution higher than 500 dpi.

Pores are formed in the sixth month of gestation due to the sweat-gland ducts reaching the

surface of the epidermis. Once the pores (perspiration is exuded from these) are formed, they

are fixed on the ridges and there can be between 9 to 18 pores along a centimetre of ridge

[69].


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Figure 32: Fingerprint Scanner & Scanned Fingerprint.

Figure 33: Minutiae patterns.


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Figure 34: Fingerprint Features.

Figure 35: Three levels of fingerprint features.

V.4. Image Quality Tenprint (famous fingerprinting system) applications require an image with detail sufficient

for extracting the image feature characteristics of minutiae, direction of ridge flow, patterns,

etc. Finger images may be categorized as missing, bandaged, poor quality, fair quality, or


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good quality. A missing finger means that the finger could not be printed, most probably

because it had been amputated. AFIS coders categorize fingerprint images as poor, fair, or

good quality. These categories are generally determined by the number of minutiae extracted

from a finger image. A poor-quality image may initiate a request to re-roll the subject, if

possible. Any subsequent records of that person would be checked for improved quality of

the images. Fair-quality images have image detail sufficient for identification but should be

replaced with good-quality images in the future if possible. Good-quality images meet or

exceed the standard for image quality.

There is clear ridge detail and flow, and a large number of minutiae. A good capture includes

three levels of ridge details [50]:

Level 1 detail includes the general ridge flow and pattern configuration. Level 1 detail is not

sufficient for individualization but can be used for exclusion. It may include information

enabling orientation, core and delta location, and distinction of finger versus palm.

Level 2 detail includes formations, defined as a ridge ending, bifurcation, dot, or

combinations thereof. The information of Level 2 detail enables individualization.

Level 3 detail includes all dimensional attributes of a ridge, such as ridge path deviation,

width, shape, pores, edge contour, incipient ridges, breaks, creases, scars, and other

permanent details.

Level 1 features can be used to categorize fingerprints in to major pattern types such as arc,

loop and whorl. Level 2 and level 3 features can be used to establish a fingerprint

individuality or uniqueness. High level features can usually be extracted only if the

fingerprint image resolution is high, i.e. level 3 feature extraction requires images with more

than 500ppi resolution. It can be noted that all these features are unvarying.

The characteristics of an ideal image for an AFIS search are the same as in pre-AFIS days. It

should be a clear image, rolled from one nail edge to the other, using even pressure that

results in an image in which the ridge shapes, deviations, and pore locations can be

distinguished.

The advantage with AFIS is that features such as ridge endings, bifurcations, and ridge flows

can be extracted electronically by a coder in just a few seconds. These same features can be

extracted identically time after time. AFIS systems can be used to search multiple fingers.

V.5. Approach to Recognize a Fingerprint The steps for fingerprint recognition can be generalized as follows:

Acquisition, where the image is obtained from hardware or a file;

Pre-processing, which may include thinning, noise reduction, image enhancements

and error correction;


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Structural extraction, where global and local structures may be found;

Post-processing, where the structures are converted into a more useful format;

Matching, where fingerprints are compared against a database.

The reliability of any automatic fingerprint recognition system strongly relies on the precision

obtained in the extraction process. Extraction of appropriate features is one of the most

important tasks for a recognition system.

Most of the fingerprint recognition systems rely on minutiae matching algorithms. Although

minutiae based techniques are widely used, because of their temporal performances, they do

not perform so well on low quality images and in the case of partial fingerprint they might

not be used at all. Therefore, when comparing partial input fingerprints to pre-stored

templates, a different approach is needed.

Classic pattern-matching methods may achieve very high scores in false acceptance rate

(FAR) and false rejection rate (FRR) but are still not good enough to attain a 100% correct

acceptance rate.

Soft computing can help to achieve better result for these types of case. Different soft

computing tools can be applied in different phases of pre-processing.

Figure 36: Stages of the fingerprint recognition process.


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Figure 37: Fingerprint Recognition System.

V.6. Image Acquisition The first stage of any vision system is the image acquisition stage. Image acquisition is

hardware dependent.

A number of methods are used to acquire fingerprints. Among them, the inked impression

method remains the most popular one.

Inkless fingerprint scanners are also present eliminating the intermediate digitization process

[40].

The basic two-dimensional image is a monochrome (greyscale) image which has been

digitized.

An image can be described as a two-dimensional light intensity function f(x,y) where x and y

are spatial coordinates and the value of f at any point (x, y) is proportional to the brightness or

grey value of the image at that point.

A digitized image is one where

spatial and greyscale values have been made discrete

intensity is measured across a regularly spaced grid in x and y directions

intensities are sampled to 8 bits (256 values)


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The method chosen for acquisition of a fingerprint image depends on many different factors,

including the cost and reliability of an input device.

Essentially the performance of a fingerprint recognition system basically depends upon the

quality of the input image.

Since the images acquired with different kinds of sensors are not of the perfect quality, they

can’t be used directly for the matching. Therefore to ensure the accurate working of the

system the image is first enhanced.

A multi-resolution fingerprint acquisition device (or sensor) must be cost-effective but should

particularly be able to acquire fingerprint images at multiple resolutions without any negative

impact on the quality of the image [69].

There are generally three kinds of fingerprint sensors: solid state, ultrasound, and optical [69],

[70].

Solid-state sensors are small and inexpensive but cannot capture high-resolution images [71].

Ultrasound sensors can capture high-resolution images but are usually bulky and expensive

[72].

Optical sensors can capture a variety of different image resolutions, varying in a range of

sizes and prices. They are easy to implement and have been found to have a high degree of

stability and reliability [75].

Most of the systems are thus equipped with an optical fingerprint sensor.

While there are also several different ways to implement optical fingerprint sensors, the

oldest and most widely used way [69] to implement the sensor is frustrated total internal

reflection (FTIR).

As shown in Figure 38, an FTIR-based fingerprint sensor consists of a light source, a glass

prism, a lens, and a charge-coupled device (CCD) or complementary metal–oxide–

semiconductor camera.

When users put their fingers on the surface of the glass prism, ridges absorb light, and so,

they appear dark, whereas valleys and the fine details on ridges reflect light and thus appear

bright.

Different resolutions can be obtained by simply adjusting the distance between the glass

prism and the lens and the distance between the lens and the camera.


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Figure 38: Operation of an FTIR-based fingerprint sensor.

Figure 39: Fingerprint Scanning.

Figure 40: Fingerprint Matching Mechanism.


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Scanned fingerprints are subject to distortions that must also be taken into account including

rotation, translation, non-linear scaling and extraneous or missing minutiae between matching

fingerprints. This creates difficulty in the matching phase because it causes the minutiae to

differ between two identical fingerprints.

V.7. Pre-processing

This is an essential part of fingerprint recognition. In this step the image is made ready for the

actual matching. The input of this phase is the original fingerprint image and the final output

of this step is the extracted dataset of that image.

Few important parts of this phase are:

Noise reduction: Noise is an unwanted perturbation to a wanted signal. Image noise is

generally regarded as an undesirable by-product of image capture. Noise reduction is the

process of removing noise from a picture (here it is the fingerprint image). There are

many different types of filtering methods like median filter, global and adaptive

thresholding, which can be used to reduce the noise [42].

Image normalization: The objective of this stage is to decrease the dynamic range of the

gray scale between ridges and valleys of the image in order to facilitate the processing of

the following stages.

The processing of fingerprint normalization can reduce the variance in gray-level values

along ridges and valleys by means of adjusting the gray-level values to the predefined

constant mean and variance. And normalization can remove the influences of sensor noise

and gray-level deformation.

Let I(i,j) denote the gray-level value of pixel (i,j) in acquired image, the size of fingerprint

image is m x n , M and V are the estimated mean and variance of input fingerprint image,

respectively, N(i, j) denotes the normalized gray-level value at pixel (i, j).

Them the normalized image is defined as follows:

N(i,j)= .............................. (1)

Where, M0, and V0 are the expected mean and variance values, respectively.

Normalization is a pixel-wise operation and does not change the ridge and valley

structures.

Works are also going on in frequency domain for fingerprint pre-processing.


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V.8. Matching Matching is a key operation of most of the current fingerprint identification systems. One of

the most important objectives of fingerprint systems is to achieve a high reliability in

comparing the input pattern with respect to the database pattern. 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.

So fingerprints from the same finger may sometimes look quite different whereas fingerprints

from different fingers may appear quite similar.

(a)

(b)

Figure 41: Fingerprint Matching: (a) Fingerprint matching steps, (b) Fingerprint authentication steps.


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Figure 42: Matching; Verification and Identification.

V.9. Different Types of Fingerprint Recognition System

Fingerprint Matching Types are mainly of two types:

Image-based matching: Image-based matching uses the entire gray scale fingerprint

image as a template to match against input fingerprint images.

Graph-based matching: Graph-based matching represents the minutiae in the form of

graphs.

Different Approaches Used for Recognition of Fingerprint:

A. Heuristic Approach

i) Singular Point Based Method: Some of the rule-based approaches were based on

the singularity features, global ridge structure information and sometimes the others

used a combination of singularity and ridge features.

ii) Global Ridge Structures: Another technique that has been proposed for a heuristic

classification system is based on the representation of ridge structures.


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B. Structural Approach

i) Syntactic approaches: A syntactic method is drawn between the structure of the

input data’s features and the production rules.

ii) Graph Matching: Relational graph approaches have interesting properties such as

invariance to rotation and displacement, and the possibility of handling partial

fingerprints

C. Neural Approach: Most of the proposed neural network techniques are based on

multilayer perceptrons and use the orientation image information as an input features.

A fuzzy-network classifier is used to classify fingerprints based on singularity

features. The features used include the number of core and delta points, the

orientation of core points, the relative position of core and delta points and the global

direction of the orientation field. Another fingerprint classification system used

artificial neural networks. Most of the neural network classification systems use the

vectors from the orientation field as the features for classification.

D. Statistical Approach: In statistical approaches, a fixed-size of numerical feature

vector is derived from each fingerprint and a general-purpose statistical classifier is

used for the classification. K-nearest neighbour is one of the most widely adopted

statistical classifier that can be found.

V.10. Classification Systems For more than 100 years, fingerprint images were classified by the rules of the Henry System

or the American System. The introduction of AFIS technology, however, virtually eliminated

the need for fingerprint examiners to master either of these two principal systems.

Identification agencies no longer invest enormous sums in training and certifying tenprint

examiners in the most intricate rules for classifying finger images. Classes on the importance

of these classification systems are still provided to staff, but their usefulness in everyday

operations is on the decline.

One system that does remain in active use is the classification system depends on Fingerprint

Classification (FPC), well known to those who do not have immediate access to AFIS, this

FPC rules classify each finger of a tenprint record using a combination of patterns, ridge

counts, and whorl tracing; does not search on finger images; instead reports on finger image

descriptors contained in its classification system. There is no requirement for a digital

camera, coder, or matcher. While the Henry and American Classification Systems have a

great deal in common, they are also quite different.

The Henry System was designed by Sir Edward Henry. While working for the Indian Civil

Service in the late 1800s, he recorded the finger images of all criminals, including all ten

fingers, a procedure unique at the time. He developed a classification system, composed of

1,024 primary classifications that assigned each of the ten fingers a unique number beginning


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with the right thumb as finger 1 to the right little finger as finger number 5. The left thumb

was finger number 6, through to the left little finger, finger number 10.

The American System was developed by Captain James Parke. It was a departure from the

traditional Henry System (and, interestingly, was not named after its chief proponent) by

providing a different score for each finger that was repeated on each hand. While the Henry

System of classification used values derived from the odd/even finger numbers, the American

Classification System was based on the hand.

Now a days, AFIS classify fingerprint images in few distinct pattern classes and the common

workflow is shown in figure 43.

Figure 43: Common workflow for fingerprint classification systems.


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V.11. Filing Systems The classification process used determined slots where the card would be physically filed,

with similarly classified cards housed together. The cards were filed based completely on

classification. Identification sections would have at least two fingerprint files: a master

fingerprint file and a secondary file. The master file held one tenprint card per individual.

Usually this was the first or original tenprint card. Assigning these records to a unique

location made it easier to find and remove the physical card as well as the electronic Rap

Sheet. Fingerprint cards were stored in various types of filing cabinets, including specialized

rotary files that would move a tray of cards into a horizontal position for easier access.

Physical cards required tremendous accountability for the location of the card. The cards

themselves, being a paper product, were subjected to fire, heat, humidity, and other

environmental factors.

AFIS (Automatic Fingerprint Identification Systems) changed all of this.

With AFIS, pattern recognition software, as well as examiners, classifies images by one of

four pattern types (whorl, arch, right loop, left loop, (Figure 44)) instead of by the Henry or

American fingerprint classification rules. There are no secondary classifications; there are no

complicated rules. AFIS coders can determine both the pattern type and minutiae placement.

In addition, with AFIS systems, fingerprint cards may or may not physically exist.

If they do exist, they may be retained at an off-site facility.

The images from the electronic cards can be displayed on a screen or printed onto card stock.

AFIS examiners no longer have to wait for a physical tenprint card; the card information is as

near as a computer terminal connected to the AFIS.

The cards cannot be misfiled or subject to deterioration due to the heat and humidity found in

an office building.

The information can be virtually retrieved, reviewed, and returned.

There is no wasted paper and no file cabinets that must be searched through taking up space.

This is one of the major advantages of AFIS.


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(a) (b)

(c) (d)

Figure 44: AFIS Pattern Types: (A) Arch; (B) Left Loop; (C) Right Loop; (D) Whorl.

V.12. Soft Computing

There are many methods in the literature for fingerprint identification using minutiae as a

feature. But there is not much work done using soft computing tool such as neural network.

Conventional computing or often called as hard computing, requires a precisely stated

analytical model and often a lot of computation time. Many analytical models are valid for

ideal cases, and real world problems exist in a non-ideal environment.

Soft computing differs from conventional (hard) computing in that, unlike hard computing, it

is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role

model for soft computing is the human mind. The guiding principle of soft computing is:

Exploit the tolerance for imprecision, uncertainty, partial truth, and approximation to achieve

tractability, robustness and low solution cost. Soft computing may be viewed as a foundation

component for the emerging field of conceptual intelligence. Few soft computing tools are:


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Fuzzy Systems, Neural Networks, Evolutionary Computation, Machine Learning and

Probabilistic Reasoning.

A use of wavelet is that of smoothing/de-noising data based on wavelet coefficient

thresholding, also called wavelet shrinkage. By adaptively thresholding the wavelet

coefficients that correspond to undesired frequency components smoothing and/or de-noising

operations can be performed. Then the output of this process can be fed into the system as

input for further processing.

Fuzzy logic is a form of many-valued logic derived from fuzzy set theory to deal with

reasoning that is fluid or approximate rather than fixed and exact.

Artificial neural networks may either be used to gain an understanding of biological neural

networks, or for solving artificial intelligence problems without necessarily creating a model

of a real biological system. Real life applications and the tasks which can be solved using

artificial neural networks include classification, including pattern and sequence recognition;

novelty detection and sequential decision making.

In the field of artificial intelligence, neuro-fuzzy refers to combinations of artificial neural

networks and fuzzy logic. It’s currently hypothesized that the human brain recognition

system for biometrics works neuro-fuzzily. Therefore, implementing a system, with added

neuro fuzzy features, or implementing a neuro-fuzzy system for biometrics (here in our case

for fingerprints), will improve the efficiency of the recognition system.

Automatic Fingerprint Identification System (AFIS) can be made more accurate using soft

computing tools. The most used fingerprint recognition systems in current days depend on

minutiae extraction. Different soft computing tools can be used in different phases of

fingerprint feature extraction, classification and matching. Evolutionary optimization

methods can be used for searching the database. In the minutiae extraction phase only

extracting minutiae does not help to get enough information about the minutiae, so aligning

minutiae becomes a necessary step. Fuzzy techniques can be used in minutiae alignment. For

the hierarchical classification of fingerprints neural network can be used. We can set four

classes for hierarchical classification i.e. arch, left loop, right loop and whorl; back

propagation neural network can be used for fingerprint identification with fuzzy techniques

incorporated for hierarchical classification and minutiae detection. Noise is a big issue in

fingerprint extraction and matching. Wavelets can be used in removing noise from the

fingerprint image.

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