Coarse Fingerprint Registration Using Orientation Fields
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Coarse Fingerprint Registration Using Orientation Fields Neil Yager School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia Email: [email protected]
Adnan Amin School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia Email: [email protected] Received 9 December 2003; Revised 2 August 2004 The majority of traditional research into automated fingerprint identification has focused on algorithms using minutiae-based features. However, shortcomings of this approach are becoming apparent due to the difficulty of extracting minutiae points from noisy or low-quality images. Therefore, there has been increasing interest in algorithms based on nonminutiae features in recent years. One vital stage in most fingerprint verification systems is registration, which involves recovering the transformation parameters that align features from each fingerprint. This paper investigates the use of orientation fields for registration; an approach that has the potential to perform robustly for poor-quality images. Three diverse algorithms have been implemented for the task. The first algorithm is based on the generalized Hough transform, and it works by accumulating evidence for transformations in a discretized parameter space. The second algorithm is based on identifying distinctive local orientations, and using these as landmarks for alignment. The final algorithm follows the path of steepest descent in the parameter space to quickly find solutions that are locally optimal. The performance of these three algorithms is evaluated using an FVC2002 dataset. Keywords and phrases: fingerprint registration, fingerprint verification, orientation fields, biometrics, FVC2002.
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INTRODUCTION
Fingerprints have been used as a means of personal identification for over a century. Traditionally, the driving force behind advancements in fingerprint technology has been law enforcement agencies and forensic scientists. Using fingerprints lifted at a crime scene to identify suspects can be a crucial step during a criminal investigation. Consequently, massive fingerprint databases have been collected by law enforcement agencies around the world. For example, the FBI maintains the world’s largest fingerprint database, containing more than 200 million prints. The administration and querying of such large databases relies heavily on automated systems, thereby motivating the early research efforts in the field. Another application of fingerprint-based identification that has emerged more recently is biometric systems. Biometrics is the automatic identification of an individual based on his or her physiological or behavioral characteristics. The ability to accurately identify or authenticate an individual based on these characteristics has several advantages over traditional means of authentication such as knowledge-based (e.g., password) or token-based (e.g., key) authentication [1].
Example applications of biometric systems include building access systems, ATM
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