Now a day’s in IT technology, security is needed and it plays an important role in IT applications and security solution applications. For maintaining security BIOMETRICS provides an important role and the name it defines as ‘bio’ is related to biological study and where ‘metric’ know as measurement. Where, Finger print identification is a technique we used in biometrics and it is most successful oldest method applicable in abundant uses. Everyone has own unique immutable fingerprints. At which finger print is mainly consist of a furrows and series of ridges on upper layer of a finger. A fingerprint is mainly identify by the furrows and ridges and minutiae points, where an minutiae points can be obtained by ridges characteristics where they be contained in ridge bifurcation and ending. An finger print identification is mainly applicable in investigations.
A fingerprint identification is a process for identify persons these can be applicable from last 19th century’s. Francis Galton defines the characteristics points of finger print identification and these points are foundation of identification and expand over past centuries. In 1960 finger print identification starts with computing technology, with those computers a Galton point subnet that refers to minutiae has implemented for development of fingerprint technology. Federal Bureau of Investigation (FBI) uses the fingerprint identification in 1969, after that it has fastly developed in many more manual process uses. After that National Bureau of Standards has
Connect with FBI for processing the automate fingerprint identification and presently the development process is going on National Institute of Standards and Technology. After a few years the NIST was focus on developments of fingerprint identification in digital link with the effects of image quality and matching and minutiae extractions. For human search narrowing uses the M40 algorithm these algorithms were introduced by FBI and these was implemented by NIST. The M40 algorithm was proved successfully and trained for human technicians for significant calculations of small set of images. In 1981, fingerprint technology was improved to Automated Fingerprint Identification System with different systems on USA and other countries. On this evaluation of all communication systems were overlooked with each other, that means collection of fingerprint in one system the other system cannot search against the other these standards need more develop in fingerprint identification. In 1994, fingerprint identification was developed the automated integrated fingerprint identification system with most important challenges. Where the challenges that implement in fingerprint identification was digital fingerprint and ridge characteristics extractions and pattern matching model system performance.
In fingerprint identification, a finger mainly looks with black series lines with friction ridges with high portion peaking and these ridges looks white space at less portion of ridge friction. The fingerprint identification mainly consists of minutiae points and ridges and bifurcations. The following figure shows the fingerprint characteristics features of minutiae and other characteristics of fingerprint.
F2: Other fingerprint characteristics
An overall data can be obtained from fingerprint ridges with friction flow and the feature presence is obtained by the individual path ridges. Some important fingerprint features were developed from AFIS. Such as it does not provide a continuous flow in pattern in friction ridges these frequently obtained in characteristics of ridges in ending and dividing and dots. The main propose of AFIS designing is to clarify the overall flow of ridges with classification and minutiae extractions of fingerprint. Fingerprint identification is mainly obtained by hardware and software technologies.
In hardware implementation in fingerprint identification can be contained by different types of sensors they are optical and capacitive and thermal and these are mainly applicable in digital picture collection on upper layer of a finger. Now a day’s optical sensors are using commonly for fingerprint image. A sensor which measures the capacitances that can be depend upon evaluate the pixel value these types of sensors is knows as capacitive sensors and it can easily evaluate the capacitance because an area of finger is more than area of valley. The thermal scanners are mainly use for the temperature measurement on digital picture at the time of finger swipe. At which some sensors contained with high frequency during the finger print scanning these type of sensors are knows as ultrasound sensors. Pastly we use some hardware technologies they named as optical sensors and solid state capacitive sensors and solid state temperature sensors and solid state electric field sensors. While in solid state sensors are very small and they can improve to nearly to machine. The latest development technology in sensors are improve with small cards like debit cards.
In software technology use in fingerprint technique is consists of two methods one is minutiae matching and another one is pattern matching method. In minutiae matching method it deals with the minutiae points and their direction of every point. In pattern matching method it deals with the similarity of the two fingerprint pictures. In fingerprint matching algorithm we are using two different techniques one is minutiae matching and other one is pattern matching technique and these two techniques are discussed below.
The modern fingerprint technology uses the minutiae matching technique. An idea starts from an same fingerprint images will prove minutiae of one picture have same corresponding to other picture at that time picture have equal minutiae points. Basically, minutiae points are equal at relative distance of other minutiae points. Points are matchup at the multiple points of one picture has same distance and another picture multiple points have equal distance. Mainly minutiae matching features are deals with the fingerprint ridges and these can be divided into three points one is short ridge and ridge ending and bifurcation.
F1: Short ridges F2; Ridge Ending F3: Bifurcation
Short Ridge: In Short ridges are define as the smaller length than the average length of ridges in fingerprint.
Ridge Ending: In ridge ending is define as the point at which ridge terminates.
Bifurcation: In bifurcation one ridge is divided into two ridges.
Basically, minutiae points are arranged with their equal position to one another with their directions in enrollment process. In matching process, the fingerprint picture identifies the minutiae points which are equal to stored fingerprint data. In these process, it first evaluate the minutiae points and after that it map the relative placement on finger at that time it contains complexity. This algorithm process cannot used for the person who having the low quality of minutiae points because the algorithm cannot use for the less quality points.
In pattern matching, it does not only compares the individual points it can compare all characteristics points on surface of finger. Mainly the characteristics are on finger densities and ridge thickness and curvature of fingerprint. While fingerprint ridges are mainly divided into three basic patterns they are Whorl and Loop and Arch.
Arch: In arch pattern the ridges are start from one side and ends on other side in between the starting and ending side that is in middle it rises like a curve shape on finger surface area.
Loop: In loop they form like a curve shape because they will start from one side and end with other side.
Whorl: In whorl ridges are form a circular shape on centre on the finger surface.
While in enrollment method, fingerprint can be extracted from the relative distance on small fingerprint section. Mainly pattern matching is use for detecting the duplicate fingerprints it will be based on the area of minutiae point and unusual combinations and low curvature and radius.
WORKING WITH BIOMETRICS
The following components are plays an important role in biometrics system. In this process, for collecting the data information on fingerprint we have to use data collection. Raw is defined as the collection of data during the fingerprint process in biometrics. After that process is finished it can matching the persons fingerprint which can be stored before, if it exists the person is authenticated and denied its access. This is process we are using in biometric process. Below figure shows the biometrics process and components in biometrics.
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For security propose fingerprint place an important role in human recognition from past years and biometric system only be present at the recent years. For the development of fingerprint standards, government and other industries had done developments on fingerprint techniques. This development over the highly quality products and faster use of devices and improve the reliability on fingerprint recognition system. Where this technique is mainly used for the government legal methods and investigation propose and science community developments and these are mainly useful for the biometrics. Behind this development we have so many reasons because biometric is not cure all security identifications. In this paper we discussed the overview of fingerprint identification and techniques we are using in fingerprint for recognition and also we discussed the how it is use for the biometric system. For determination of fingerprint industry government and other industries will done led on coming generation for fingerprint identification.
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