Performance evaluation of fingerprint verification systems

Biometric System Laboratory-DEIS, University of Bologna, via Sacchi 3, 47023 Cesena, Italy.
IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 5.78). 02/2006; 28(1):3-18. DOI: 10.1109/TPAMI.2006.20
Source: PubMed


This paper is concerned with the performance evaluation of fingerprint verification systems. After an initial classification of biometric testing initiatives, we explore both the theoretical and practical issues related to performance evaluation by presenting the outcome of the recent Fingerprint Verification Competition (FVC2004). FVC2004 was organized by the authors of this work for the purpose of assessing the state-of-the-art in this challenging pattern recognition application and making available a new common benchmark for an unambiguous comparison of fingerprint-based biometric systems. FVC2004 is an independent, strongly supervised evaluation performed at the evaluators' site on evaluators' hardware. This allowed the test to be completely controlled and the computation times of different algorithms to be fairly compared. The experience and feedback received from previous, similar competitions (FVC2000 and FVC2002) allowed us to improve the organization and methodology of FVC2004 and to capture the attention of a significantly higher number of academic and commercial organizations (67 algorithms were submitted for FVC2004). A new, "Light" competition category was included to estimate the loss of matching performance caused by imposing computational constraints. This paper discusses data collection and testing protocols, and includes a detailed analysis of the results. We introduce a simple but effective method for comparing algorithms at the score level, allowing us to isolate difficult cases (images) and to study error correlations and algorithm "fusion." The huge amount of information obtained, including a structured classification of the submitted algorithms on the basis of their features, makes it possible to better understand how current fingerprint recognition systems work and to delineate useful research directions for the future.

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    • "After 5 s, it keeps improving the results but without a big difference. The TAR and FAR achieved by the proposed mobile ECG biometric method are inferior to the ones typically attained by fingerprint biometric systems, where Cappelli et al.[49]obtained an FAR of 2.07% and a TAR of 97.93%. However, the vulnerability of fingerprint authentication technologies (i.e., failure to authenticate when the prints are damaged[4]or to be spoofed by capturing the prints left on objects[5]) motivates the study of alternative methods that do not suffer from the same weaknesses, such as ECG authentication. "
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    • "Therefore, the background database of NIST SD27 was augmented with 2,000 fingerprints from NIST SD4 [33]; 27,000 fingerprints from NIST SD14 [41]; 106,921 different rolled impressions collected by forensic experts using the same protocol used to create the database NIST SD27 [18]; and 997,337 synthetic fingerprints which were generated using the SFinGe Version 4.1 (build 1746) Demo [42]. SFinGe generates synthetic fingerprints and it has been used in several fingerprint verification competitions showing that the results on synthetic databases are comparable to the results on real databases [43]. We cannot guarantee that the results on this database are equal to using real fingerprints but this experiment gives an idea of how the algorithms might perform in a more realistic scenario. "
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