Fingerprint matching techniquesIntroduction to Fingerprint Biometric Student name: Bar TamarIn today’s world, as the usage of large scale systems as part of our daily lives has become a necessity, there is a growing need to guarantee reliable user identification. Those large-scale systems could range from banking services to online grocery shopping. Obviously, not all of those systems will be using abiometric asset as their user identification scheme, mainly due to cost and privacy issues, but some, more crucial and secured systems might need highly reliable identification. Among the identification methods applied in different systems, the most reliable one for sure would be using the person’s own bodily characteristics – biometrics. Why use biometrics?- Biometric templates are unique to an individual.- Unlike password, pin number, or smart card, they cannot be forgotten, misplaced lost or stolen.- The person trying to access is identified by his real id (represented by his unique biometric signature).Why use Fingerprint as a biometric?- Fingerprint scanning has a high accuracy rate when users know how to use the system.- Fingerprint authentication is a good choice for in-house systems where training can be provided to users and where the device is operated in acontrolled environment.- Small size of fingerprint scanners, ease of integration can be easily adapted for appliances (keyboards, cell phones, etc).- Relatively low costs make it an affordable, simple choice for workplace access security.- The most common biometric used.- Has been credited as reliable to the extent of being admissible as the sole proof in a court of lawAmong all the biometric techniques, fingerprint-based identification is the oldest method, and has been successfully used in numerous applications. Everyone is known to have unique, immutable fingerprints - a property that can be used for identification.The process of fingerprint identification includes the following stages:scanning (capture, acquisition), extraction (process), comparison, and final match/non-match decision. Before getting to know and understand the automated process of fingerprint identification we will get to know some of the general terms used in this field – that way we can understand what the latter processes might involve.A fingerprint is made of a series of ridges and furrows on the surface of thefinger. The uniqueness of a fingerprint can be determined by the pattern of ridges and furrows as well as the minutiae points, Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending.Here are some examples for minutiae points that are common: In dark color we see the ridges and in white the furrows.Pic. 1 Pic. 2 Pic. 3 Pic. 4Pic. 5 Pic. 6 Pic. 7 Pic. 8Pic. 9 Pic. 10 Pic. 11 Pic. 12Pic. 13 Pic. 14 Pic. 15 Pic. 16Pic.1 Beginning or ending L Pic.9 Double whorl RLRLPic.2 Single bifurcation L Pic.10 Single bridge RLPic.3 Double bifurcation LL Pic.11 Twin bridge RLLRPic.4 Triple bifurcation type1LLL Pic.12 Interval RLPic.5 Triple bifurcation type2LLL Pic.13 Point RLPic.6 Triple bifurcation type3LLL Pic.14 Through line LRPic.7 Hook RL Pic.15 Crossing LRPic.8 Single whorl RL Pic.16 Side contact RLFingerprint ScanningFingerprint scanning is the acquisition and recognition of a person’s fingerprint characteristics for identification purposes. This allows the recognition of a person through quantifiable physiological characteristics that verify the identity of an individual.There are basically two different types of finger-scanning technology that make this possible.1. One is an optical method, which starts with a visual image of a finger. 2. The other uses a semiconductor-generated electric field to image a finger. There are various methods for identifying fingerprints. They include traditional police methods of matching minutiae, straight pattern matching, moiré fringe patterns and ultrasonic. I will describe a general way – just to explain the main idea that lies in the base of most image comparison systems.Image Recognition and EnhancementA critical step in automatic fingerprint matching is to automatically and reliably extract minutiae from the input fingerprint images. For extracting information from our image we require specific algorithms developed for the recognition of certain patterns, however, the performance of a minutiae extraction algorithm relies heavily on the quality of the input fingerprint images. In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of the fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. At the end of this process we should be able to look at a sample fingerprint not as a digital picture, but rather a collection of objects that the algorithm has defined. Since we are speaking about image recognition these algorithms are designed in such a way so they can be more robust to noise in fingerprint images and deliver increased accuracy in real-time. In a real application, the sensor, the acquisition system and the variation in performance of the system over time is very critical.Fingerprint matching techniquesFingerprint matching techniques can be placed into two categories: minutiae-based and correlation based. Minutiae-based techniques first find minutiae points and then map their relative placement on the finger. However, there are some difficulties when using this approach. It is difficult to extract the minutiae points accurately when the fingerprint is of low quality. Also this method does not take into account the global pattern of ridges and furrows. Correlation-based techniques require the precise location of a registrationpoint and are affected by image translation and rotation. The correlation-based method is able to overcome some of the difficulties of the minutiae-based approach, but, it has some of its own shortcomings.Fingerprint matching based on minutiae has problems in matching different sized (unregistered) minutiae patterns. Local ridge structures cannot be completely characterized by minutiae.It is very difficult to achieve a very low false negative rate (rejecting an authorized user) while keeping a lower false positive results (allowing access to unauthorized user) using only one technique. Many companies investigate methods to pull evidence
View Full Document
Unlocking...