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Center for Education and Research in Information Assurance and Security

Fingerprint Sensor Interoperability: Analysis of Error Rates for Fingerprint Datasets Acquired from Multiple Fingerprint Sensors

Shimon Modi - Purdue University

Sep 17, 2008

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Abstract

The last decade has witnessed a huge increase in deployment of biometric systems, and while most of these systems have been single vendor, monolithic architectures the issue of interoperability is bound to arise as distributed architectures are considered for large scale deployments. The distortions and variations introduced when acquiring fingerprint images propagate from the acquisition subsystem all the way to the matching subsystem. These variations ultimately affect performance rates of the overall fingerprint recognition system. Fingerprint images captured using the same sensor technology during enrollment and recognition phases will introduce similar distortions, thus making it easier to compensate for such distortions and reducing its effect on the performance of the overall fingerprint recognition system. However, an impact on performance is expected, but unpredictable, when different fingerprint sensor technologies are used during enrollment and recognition phases. The purpose of this study was to examine the effect of sensor dependent variations and distortions, characteristics of the sensor and characteristics of the finger skin on the interoperability matching error rates of minutiae based fingerprint recognition systems. Fingerprint images were be collected from 9 different fingerprint sensors from 190 subjects for analysis of this research study. A statistical analysis framework for testing interoperability was formulated for this research, which included parametric and non-parametric tests. The statistical analysis framework tested similarity of minutiae count, similarity of image quality and similarity of performance between native and interoperable datasets. Interoperability performance analysis was conducted on each sensor dataset and also by grouping datasets based on the acquisition technology and interaction type of the acquisition sensor. The end objective of this study was to provide greater insight into the effect of a fingerprint dataset acquired from various sensors on performance measured in terms of error rates like false non match rates (FNMR) and false match rates (FMR).

About the Speaker

Dr. Shimon Modi is Director of Research of the Biometric Standards, Performance and Assurance Laboratory at Purdue University, and has been involved in biometrics research for over five years. He received his Ph.D. in Technology in 2008. Dr. Modi’s Ph.D. dissertation was related to statistical testing and analysis of fingerprint sensor interoperability on system performance. He has a Master’s degree in Technology with specialization in Information Security from the Center for Education and Research in Information Assurance and Security (CERIAS), and a Bachelor's degree in Computer Science from Purdue University.

Dr. Modi’s research interests reside in application of biometrics to e-authentication, statistical analysis of system performance, enterprise level information security, and standards development. Dr. Modi conducted his Master’s thesis in feasibility testing of using keystroke dynamics for spontaneous password verification. Dr. Modi has co-written and contributed to 3 published books, published 9 conference proceedings, and been involved in developing a graduate level course targeted at security professionals.

Dr. Modi is actively involved in biometric standards, both at the national and international level. Dr. Modi serves as a technical editor for the BioAPI-Java project, and represents Purdue University as a voting member on INCITS M1 Biometrics standards committee.

Dr. Modi was a recipient of the Ross Fellowship (2005-2006) which is awarded with the intention of recruiting outstanding doctoral seeking students at Purdue University.

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