A SURVEY TECHNIQUES FOR THE ENHANCEMENT AND PERFORMANCE OF FINGERPRINT AUTHENTICATION ABSTRACT Authentication systems play a major role in identifying an individual The existing fingerprint authentication systems depend on specific points known as minutiae and pores for recognizing an individual Designing a reliable automatic fingerprint authentication system is still very challenging since not all fingerprint information is available Further the information obtained is not always accurate due to cuts scars distortion Moreover the existing fingerprint authentication systems do not utilize other significant minutiae information which can improve the accuracy of the fingerprint authentication system Works when the minutiae information is sparse and produces satisfactory matching accuracy in the case when minutiae information is unavailable Mixed template is cancellable and serves as a new virtual identity To reduce the error rate Gabor Filter is used Testing the templates is done using 2 methods correlation based and minutiae based INTRODUCTION Investigating the methods for generating MasterPrints Two approaches Where the print is selected from an existing dataset of Real fingerprints 

Where the print is generated Synthetically For the first approach a fixed dataset is used as the training dataset from which the MasterPrint is sampled These MasterPrints from a dataset are termed as SAMP The synthetically created MasterPrints in the next approach are termed as SYMP Both approaches are designed for a minutiae based fingerprint authentication system and a detailed description of them is presented below INCORPORATING MINUTIAE DISCRIMINABILITY 1 Traditional minutiae matching algorithms assume that each minutia has the same discriminability The fingerprint minutiae tend to form clusters and minutiae points that are spatially close tend to have similar directions with each other When two different fingerprints have similar clusters there may be many Ill matched minutiae The other one is that false minutiae may be extracted due to low quality fingerprint images which result in both high FAR and high FRR Analyze the minutiae discriminability from the viewpoint of global spatial distribution and local quality Firstly I propose an effective approach to detect such cluster minutiae which of low discriminability and reduce corresponding minutiae similarity Secondly I use minutiae and their neighbors to estimate minutia quality and incorporate it into minutiae similarity calculation In order to improve low quality fingerprint matching I propose to use neighbors of a minutia to measure its quality and incorporate the quality to minutiae similarity calculation 

EVIDENTIAL VALUE OF AUTOMATED LATENT FINGERPRINT 2 In light of the empirically demonstrated nonzero error rates of latent fingerprint matching and instances of critical errors leading to undue incarceration of innocent individuals it is crucial to establish the evidential value of a latent fingerprint comparison I present a framework to capture the evidence of a given fingerprint match score in terms of nonmatch probability The variation of NMP values associated with fingerprint databases having specific fingerprint characteristics The NMP values obtained from different partitions of a fingerprint database Ire compared using a measure called the conclusiveness that estimates the significance of evidence associated with an NMP value Due to paucity of a large training set of latents PORES AND RIDGES 4 Three different levels Pattern Minutiae Points and pores and ridge contours PORES features are used to assist in identification Automated Fingerprint Identification Systems currently rely only on Pattern and Points features Many sensors are now equipped with dual resolution scanning capability pores and ridges are automatically extracted using Gabor filters and WFT FINGERPRINT IDENTIFICATION 7 In GPU hardware the GPUs may drastically improve the efficiency of fingerprint identification this is particularly true for modern local minutiae matching algorithms which are Ill suited for parallel implementations The design of an effective GPU fingerprint identification approach requires to address the following issues limiting data transfer compact data representation optimizing memory allocation and access defining a computation flow that fully exploits the hardware capabilities

Algorithm is based on Minutia Cylinder Code recently introduced as a convenient way to represent fingerprint minutiae WAP 3 A wolf attack probability as a new measure for evaluating security of biometric authentication systems The wolf attack is an attempt to infect a victim by feeding wolves into the pc to be attacked The wolf means an input value which can be falsely accepted as a match with multiple templates WAP is defined as a maximum success probability of the wolf attack with one sample 

IMPACT OF FAKE FINGERS 5 Fake fingers should be crucial for authentication based on fingerprint systems Security evaluation has been rarely detected in case of artificial fingers The number of acceptance of live fingers is greater than that of fake fingers that employ so called Real Time and detection is noted with dust fingers or electrical resistance RIDGE EXTRACTION 6 Ridge features consist of four elements length curvature direction count and type It has some advantages in that they can represent the topology content in whole ridge patterns that exist between two points and are not changed by non continuous deformation of the finger They have also defined the ridge based system in a skeletal image for extracting image CONCLUSION According to the survey the fingerprint enhancement and performance flaws has been recovered using several techniques in order to increase the accuracy and performance of authentication system Here some of the features are applied accordingly to the products resolution or fingerprint pressure based on scars and cuts on skin There are several other options required to be developed in the future to increase the identity of the fingerprint

Write and Proofread Your Essay
With Noplag Writing Assistance App

Plagiarism Checker

Spell Checker

Virtual Writing Assistant

Grammar Checker

Citation Assistance

Smart Online Editor

Start Writing Now

Start Writing like a PRO