On Line Fingerprint Verification
Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it is incapable of meeting today’s increasing performance requirements. An automatic fingerprint identification system (AFIS) is widely needed. It plays a very important role in forensic and civilian applications such as criminal identification, access control, and ATM card verification. This paper describes the design and implementation of an on-line fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al., which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an on-line inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of on-line verification with high accuracy.
FINGERPRINTS are graphical flow-like ridges present on human fingers. They have been widely used in personal identification for several centuries. The validity of their use has been well established. Inherently, using current technology fingerprint identification is much more reliable than other kinds of popular personal identification methods based on signature, face, and speech . Although fingerprint verification is usually associated with criminal identification and police work, it has now become more popular in civilian applications such as access control, financial security, and verification of firearm purchasers and driver license applicants. Usually, fingerprint verification is performed manually by professional fingerprint experts. However, manual fingerprint verification is so tedious, time-consuming, and expensive that it does not meet the performance requirements of the new applications. As a result, automatic fingerprint identification systems (AFIS) are in great demand . Although significant progress has been made in designing automatic fingerprint identification systems over the past 30 years, a number of design factors (lack of reliable minutia extraction algorithms, difficulty in quantitatively defining a reliable match between fingerprint images, fingerprint classification, etc.) create bottlenecks in achieving the desired performance . An automatic fingerprint identification system is concerned with some or all of the following issues: • Fingerprint Acquisition: How to acquire fingerprint images and how to represent them in a proper format. • Fingerprint Verification: To determine whether two fingerprints are from the same finger. • Fingerprint Identification: To search for a query fingerprint in a database. • Fingerprint Classification: To assign a given fingerprint to one of the prespecified categories according to its geometric appearance
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