06-03-2013, 11:55 AM
Fingerprint Recognition
Fingerprint.doc (Size: 37 KB / Downloads: 93)
Abstract
Fingerprint image analysis for automatic identification technology has been developed for use in a number of major applications. Important industries affected by this technology include network security and protection, smart money, ATM transaction, and biometric identifier systems for many major government sectors. In this paper we discuss the major components of the technology including the live-scan fingerprint subsystem, the WSQ compression algorithm, and the recognition algorithm.
Introduction
The fingerprints have been used as a mean for identifying individual for a long time because the fingerprints are unique and stay unchanged through out an individual life time. The chance of two people—even identical twins—having the same fingerprint is probably less than one in a billion. Fingerprint comparison is the most widely used method of biometric authentication and the most cost effective. Currently there are about 200 million FBI cards (10 fingerprints per cards) stored in cabinets that would fill an area of one acre. The manual effort of identifying and maintaining such a system is very cumbersome, time consuming and expensive as the number of finger print records grows at a rate from 30 to 50 thousands cards per day [1]. With the advancement of computer technology the problem of automatic finger print identification has attracted wide attention among researchers that results in automatic fingerprint identification system (AFIS) available today. Going in hands with the recognition problem
AFIS: Automatic Fingerprint Identification System
The four main components of an AFIS system is the scanner, the recognition algorithm, the search and query algorithm of the data base and the data compression algorithm.
The Live Scanner
The live scanner captures the finger print at a minimum resolution of 500 pixels per inch in both row and column and each pixel shall be gray level quantized to 8 bits. Regardless of the method and media used by the scanner, the electronic image must be sufficient quality to provide conclusive finger print comparison, successful finger classification and feature detection, and can support an AFIS search reliably. The major consideration for the scanner is whether or not it meets number test procedures that will warranty the image quality as stated in the Minimum Image Quality Requirement, Electronically Produced, Fingerprint Cards, and Appendix F- IAFIS Image Quality Specifications.
Modulation Transfer Function(MTF)
MTF is the point response of the image capturing system. For each frequency the Image Modulation (IM) is computed.
IM = (Max- Min)/ (Min-Max)
The MTF is then computed by dividing the Image Modulation by the Target Modulation.
FingerprintMatching
The pre-processing aim is to improve the quality of the image. The pre-processing has two tasks:
¨ Ridge enhancement
¨ Restoration and segmentation of fingerprint image
The pre-processing step produces a binary segmented fingerprint ridge image from an input grey scale image. The ridges have a value of ‘1’ and the rest of the image has value of ‘0’. The pre-processing steps involve
¨ Computation of orientation field
¨ foreground/background separation,
¨ ridge segmentation
Conclusion
We have presented the overview of the finger print technology which include primarily the scanner, the classification of fingerprint image in the database, the matching algorithms and the compression\decompression algorithm standardized by the FBI. Certain standard perhaps might be needed for this area before major commercial system applications can be implemented. An application which is a part of the fingerprint based biometric systems for commercial driver license has been shown.