ABSTRACT:
Now a days we are facing majority of crimes related to security issues and these arise due to the leakage of passwords or illegal authentication. At one end, there is a continous and tremendous improvement in the life style of humans while at the other end ; the technological crimes are increasing rapidly. As there is a problem, there must be a solution .The need for a compromising technology which can be adopted is highly imperative .Technologies capable of identifying each person uniquely need to be developed. The only powerful solution for the problem of illegal authentication is biometrics.

Nature has made human beings with different characterstics which may vary from one person to another .This property is made use of Biometric Technolgy to distinctly identify each person.Biometrcs is a means of using the physiological or behavioral characterstics of a person as a kind of permanent password.
This paper includes about biometrics,its operation and performance and comparison among biometric categories.Its applications in RETINAL SCAN and in FIGER AUTHENTICATION.
So,the crime in security issues are thrown by BIOMETRICS.
ABOUT BIOMETRIC :
Biometric is a ancient Greek word which means: bios
="life", metron ="measure".It is the study of automated methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits.
In information technology, biometric authentication refers to technologies that measure and analyze human physical and behavioral characteristics for authentication purposes.
Examples of physical characteristics include fingerprints, eye retinas and irises, facial patterns and hand measurements , while examples of mostly behavioral characteristics include signature, gait and typing patterns . Voice is considered a mix of both physical and

behavioral characteristics . But all biometric traits share physical and behavioral aspects.
OPERATION AND PERFORMANCE :
In a typical IT biometric system, a person registers with the system when one or more of his physical and behavioral characteristics are obtained. This information is then processed by a numerical algorithm, and entered into a database. The algorithm creates a digital representation of the obtained biometric. If the user is new to the system, he or she enrolls, which means the digital template is entered into the database. Each subsequent attempt to use the system, or authenticate, requires the biometric of the user to be captured again, and processed into a digital template. That template is compared with prestored one.The above process will repeats at time the user attempts to entering in to the system. The comparison process involves the use of a Hamming distance. Hamming distance involves comparision of bits,if the bits are dissimilar,hamming distance contains the percentage of dissimilar bits out of the number of comparisons made.
Example if two strings 101010 and 100110 then the hamming distance is two,since there is variation in “10” and “01”.
So system easily detects the fraud person by comparing the features behind it.Current technologies have widely varying
Equal Error Rates, varying from as low as 60% and as high as 99.9%.
PERFORMANCE:
Performance of a biometric measure is referred interms of the false accept rate (FAR), the false non match or reject rate (FRR), and the failure to enroll rate (FTE or FER).
The FAR measures the percent of invalid users who are incorrectly accepted as genuine users, while the FRR

measures the percent of valid users who are rejected as impostor.
In real-world biometric systems the FAR and FRR can typically be tradeoff against each other by changing some parameter. One of the most common measures of real-world biometric systems is the rate at which both accept and reject errors are equal: the equal error rate (EER), also known as the cross-over error rate (CER). The lower the EER or CER, the more accurate the system is considered to be.
Stated error rates sometimes involve idiosyncratic or subjective elements. For example, one biometrics vendor set the acceptance threshold high, to minimize false accepts.
In the trial, three attempts were allowed, and so a false reject was counted only if all three attempts failed. At the same time, when measuring performance biometrics (e.g. writing, speech etc.), opinions may differ which constitute a false reject. If a signature verification system is trained with an initial and a surname, can a false reject be legitimately claimed when it then rejects the signature incorporating a full first name?
Despite these misgivings, biometric systems have the potential to identify individuals with a very high degree of certainty.
A COMPARISION OF BIOMETRICS :
The BIOMETRICS compares each other against seven categories:

THEY ARE:
 Universality :It describes how common a biometric is found in each individual.
 Uniqueness :It shows how well the biometric separates one individual from another.
 Permanence: ->measures how well a biometric resist aging.
 Collectability :It explains how easy it is to acquire a biometric for measurement.
 Performance :It indicates the accuracy, speed, and robustness of the system capturing the biometric.
 Acceptability : It indicates the degree of approval of a technology by the public in everyday life.
 Circumvention :It is how easy it is to fool the authentication system.
They are ranked by biometric based on the categories as being either low, medium, or high. A low ranking indicates poor performance in the evaluation criterion whereas a high ranking indicates a very good performance.
ISSUES AND CONCERNS:
As with many interesting and powerful developments of technology, excessive concern with the biometric may have the effect of eclipsing a more general critical faculty.
Identity theft and privacy issues :
Concerns about Identity theft through biometrics use have not been resolved.
For example:
If a person's credit card number is stolen, it can cause them great difficulty with following factors:
SINGLE FACTOR :

In single factor,just knowing the credit card number and its expiration date can sometimes enough to use a stolen credit card successfully.
TWO-FACTOR : In this security solutions requires something you know plus something you have;
For example, a debit card and a personal Identification
Number(PIN)OR BIOMETRIC .
By accessing biometric data one may think that the financial or personal information of a person may hacked.But it ignores a key operational factor inherited to all biometrics-based security solutions , biometric solutions are based on matching. At the point of transaction,the person image will be scanned ,means “live” image.THE obtained image is matched with prestored static
"match template" created when the user originally enrolled in the security system. Most of the available biometric systems address the issues of ensuring that the static enrollment sample has not been tampered with IE, using hash codes and encryption, so the problem is effectively limited to cases where the scanned "live" biometric data is hacked.
Even then, most competently-design solutions contain antihacking routines.
APPLICATIONS OF BIOMETRIC :
RETINAL SCAN :
A Retinal scan is a biometric technique that uses the unique patterns on a person's retina to identify them.
The human retina is stable from birth to death, making it the most accurate biometric to measure. It has been possible to take a retina scan since the 1930s, when research suggested that each individual had unique retina patterns. The research was validated and we know that the blood vessels at the back of the eye have a unique pattern,

from eye to eye and person to person. A retinal scan involves the use of a low-intensity light source and coupler that are used to read the blood vessel patterns, producing very accurate biometric data. It has the highest crossover accuracy of any of the biometric collectors, estimated to be in the order of 1:10,000,000.
Development of the technology has taken longer than expected and for many years the process of taking a retinal scan was measured in 10 seconds. New technology is capable of capturing a retinal scan in less than 2 seconds.
ADVANTAGE :Some biometric identifiers, like fingerprints, can be spoofed. This is not the case with a retina scan.
The retina of a deceased person quickly decays and cannot be used to deceive a retinal scan. It is for this reason that retina scan technology is used for high end access control security applications.
Fingerprint authentication:
DERMATOGLYPHICS:
DERmatoglyphics (from ancient Greek derma = "skin", glyph = "carving") is the scientific study of fingerprints. The term was coined by Dr. Harold Cummins, the father of fingerprint analysis .
 Fingerprint authentication refers to the automated method of verifying a match between two human fingerprints. Fingerprints are one of many forms of biometrics used to identify an individual and verify their identity . This article touches on two major classes of algorithms (minutia and pattern) and four sensor designs (optical, ultrasonic, passive capacitance, and active capacitance).
PATTERNS:

Patterns include aggregate characteristics of ridges.
The three basic patterns of fingerprint ridges are the
Arch, Loop, and Whorl.
ARCH: An arch is a pattern where the ridges enter from one side of the finger, rise in the center forming an arc, and then exit the other side of the finger.
LOOP :The loop is a pattern where the ridges enter from one side of a finger, form a curve, and tend to exit from the same side they enter.
WHORL :In the whorl pattern,ridges form circularly around
a central point on the finger.
Scientists have found that family members often share the same general fingerprint patterns, leading to the belief that these patterns are inherited.
The arch pattern : The loop pattern :
The whorl pattern :

MINUTIA FEATURES : Minutia points, which are unique features found within the patterns.
The major minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or dot).
Ridge ending :It is the point at which a ridge terminates.
Bifurcation :These are points at which a single ridge splits into two ridges.
Short ridges(DOT) :Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint.
 Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have been shown to be identical.
Ridge ending Bifurcation

Short ridges(DOT)
Fingerprint sensors :
A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan. This live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. This is an overview of some of the more commonly used fingerprint sensor technologies.
Optical sensor :
Optical fingerprint imaging involves capturing a digital image of the print using visible light. This type of sensor is, in essence, a specialized digital camera. The top layer of the sensor, where the finger is placed, is known as the touch surface. Beneath this layer is a light-emitting phosphor layer which illuminates the surface of the finger.
The light reflected from the finger passes through the phosphor layer to an array of solid state pixels (a charge coupled device) which captures a visual image of the fingerprint. A scratched or dirty touch surface can cause a bad image of the fingerprint. A disadvantage of this type of sensor is the fact that the imaging capabilities are affected by the quality of skin on the finger. For instance, a dirty or marked finger is difficult to image

properly. Also, it is possible for an individual to erode the outer layer of skin on the fingertips to the point where the fingerprint is no longer visible. However, unlike capacitive sensors, this sensor technology is not susceptible to electrostatic discharge damage.
Capacitance sensor :
Capacitance sensors utilize the principles associated with capacitance in order to form fingerprint images. The two equations used in this type of imaging are:
, where
C is the capacitance in farads.
Q is the charge in coulombs.
V is the potential in volts.
E0 -> permittivity of free space,measured in farad/metre
εr is the dielectric constant of the insulator used
A-> area of each plane electrode, measured in square metres d->separation between the electrodes, measured in metres
In this method of imaging, the sensor array pixels each act as one plate of a parallel-plate capacitor, the dermal layer (which is electrically conductive) acts as the other plate, and the non-conductive epidermal layer acts as a dielectric.
Passive capacitance:
A passive capacitance sensor uses the principle outlined above to form an image of the fingerprint patterns on the dermal layer of skin. Each sensor pixel is used to measure the capacitance at that point of the array. The capacitance varies between the ridges and valleys of the fingerprint

due to the fact that the volume between the dermal layer and sensing element in valleys contains an air gap. The dielectric constant of the epidermis and the area of the sensing element are known values. The measured capacitance values are then used to distinguish between fingerprint ridges and valleys.
Algorithms :Matching algorithms are used to compare previously stored templates of fingerprints against candidate fingerprints for authentication purposes. In order to do this either the original image must be directly compared with the candidate image or certain features must be compared.
Pattern-based (or Image-based) algorithms :
It compares the basic fingerprint patterns between a previously stored template and a candidate fingerprint.
This requires that the images be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centers on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.
Minutia-based algorithms :
It compares several minutia points extracted from the original image stored in a template with those extracted from a candidate fingerprint. Similar to the pattern-based algorithm, the minutia-based algorithm must align a fingerprint image before extracting feature points. This alignment must be performed so that there is a frame of

reference. For each minutia point, a vector is stored into the template in the form: mi = (type,xi,yi,θi,W) where mi is the minutia vector type-> type of feature(ridgeending,bifurcation,short ridge) xi is the x-coordinate of the location yi is the y-coordinate of the location
θi is the angle of orientation of the minutia
W-> weight based on the quality of the image at that location
It is important to note that an actual image of the print is not stored as a template under this scheme. Before the matching process begins, the candidate image must be aligned with the template coordinates and rotation.
Features from the candidate image are then extracted and compared with the information in the template. Depending on the size of the input image, there can be 10-100 minutia points in a template. A successful match typically only requires 7-20 points to match between the two fingerprints.
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