04632892 - 2008 5th International Multi-Conference on...

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2008 5th International Multi-Conference on Systems, Signals and Devices MINUTIAE EXTRACTION FOR FINGERPRINT RECOGNITION Yusra AI-Najjar, Alaa Sheta Information Technology Department AI-Balqa Applied University Salt, Jordan. usra7@yahoo.com, asheta2@yahoo.com ABSTRACT Automatic Personal Identification (API) represents a challenge for tremendous life applications such as in pass- ports, cellular telephones, automatic teller machines, and driver licenses. It is important to achieve a high degree of confidence when handling such types of application. Bio- metrics is being more and more adopted in such cases. In the past years, the developnlent of fingerprint identifica- tion systems has received a great deal of attention. The goal of this paper is to represent a complete identification process for fingerprint recognition throughout the extract- ing of matching minutiae. The performance of the pro- posed system is tested on a database with fingerprints from different people and experimental results are presented. Index Terms- Fingerprint, minutiae extraction, ter- mination, bifurcation. 1. INTRODUCTION Fingerprint technology is the most widely used form of biometric technology. Traditional knowledge-based (pass- word or personal Identification Number (PIN» and token- based (password, driver license, and ID card) identifica- tions are prone to fraud because PINs may be forgotten or guessed by others and the token may be lost or stolen [1]. Therefore, biometric, which refers to identifying an indi- vidual based on the physiological or behavioral character- istics has been more reliable. For decades, fingerprints have been in use for biometric recognition because of their high immutability and individuality [2]. Immutability refers to the persistence of the fingerprints over time whereas individuality is related to the uniqueness of ridge details across individuals. The probability that two fingerprints are alike is 1 in 1.9 x 10 15 [3]. These features give the fin- gerprint its importance; they are extremely effective where high degree of security is an issue. Minutiae detection can be categorized into two types: global and local. A global representation gives an overall characteristic of the finger \vhere a single representation is valid for the entire fingerprint. Whereas, a local repre- sentation consists of segments derived from regions of the fingerprint. Typically global representations are used for 978-1-4244-2206-7/08/$25.00 ©2008 IEEE classification of fingerprints into different categories such as right loop, left loop, and arch etc. The global classifica- tion schema of fingerprints in provided in [4] and shown in Figure 1. Major local representations of fingerprints are based on finger ridges. Fingerprints possess many features called local fea- tures. Minutiae are minute details of the fingerprint [5].
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This note was uploaded on 12/30/2010 for the course EE 111 taught by Professor Soaahaib during the Spring '10 term at Lahore University of Management Sciences.

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04632892 - 2008 5th International Multi-Conference on...

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