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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    • "This kind of acquisition artifacts may deeply affect the performance of biometric systems and hence, decrease their use in real life applications. Moreover, the impact of quality on the system overall performace is also presented by the results of the FVC seriers of competitions (FVC in 2000, 2002, 2004 and 2006) [4]. "
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    ABSTRACT: The quality of biometric raw data is one of the main factors affecting the overall performance of biometric systems. Poor biometric samples increase the enrollment failure and decrease the system performance. Hence, controlling the quality of the acquired biometric raw data is essential in order to have useful biometric authentication systems. Towards this goal, we present a generic methodology for the quality assessment of image-based biometric modality combining two types of information: 1) image quality and 2) pattern-based quality using the scale-invariant feature transformation (SIFT) descriptor. The associated metric has the advantages of being multimodal (face, fingerprint, and hand veins) and independent from the used authentication system. Six benchmark databases and one biometric verification system are used to illustrate the benefits of the proposed metric. A comparison study with the National Institute of Standards and Technology (NIST) fingerprint image quality (NFIQ) metric proposed by the NIST shows the benefits of the presented metric.
    EURASIP Journal on Image and Video Processing 12/2015; 2015(1). DOI:10.1186/s13640-015-0055-8 · 0.74 Impact Factor
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    • "It requires the network management system to provide a variety of management functions without visibly sacrifice any network performance. On the other hand, the application of network management system may lead to unintended outcome of the network[3] [4]. For example, the management system may accidentally block normal services when trying to stop the abnormal ones or misidentify a normal user to be a malicious one. "
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    ABSTRACT: The growing of network applications and the advances in network services demand for more complex and flexible network management systems. It becomes difficult to evaluate the performance of such a system using the traditional, simple and arbitrary evaluation methods especially when the system is designed for a specific purpose. Systematic evaluation models should be built to evaluate if a system meets the specified requirements while does not sacrifice much of the network performance. In this paper, we propose a three-dimensional evaluation model for network management system which takes cost, impact, as well as operation into consideration at the same time. We define evaluation index as a performance indicator, which is detailed elaboration for the proposed model. We believe that the three-dimensional evaluation for network evaluation can be better suited in nowadays network environment.
    Procedia Computer Science 12/2013; 17:888-892. DOI:10.1016/j.procs.2013.05.113
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    • "Yet, instead of studying what brings us together, biometrics is concerned with what sets us apart [1]. The systematic collection of physical and behavioral characteristics for the purposes of identification dates as far back as 1858, with the collection of handprints to identify workers [2], and has since expanded to include such features as: fingerprints [3], palm prints, action, hand geometry, facial structure [4], signature, speech, gait, and iris patterns [5]. Despite this long history, automated biometric systems have only become feasible in the recent past [6], and are limited by several factors: accuracy, counterfeit-resistance, speed, and cost. "
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    ABSTRACT: This paper presents an objective evaluation of previously unexplored biometric techniques utilizing patterns identifiable in human eye movements to distinguish individuals. The distribution of primitive eye movement features are compared between eye movement recordings using algorithms based on the following statistical tests: the Ansari-Bradley test, the Mann-Whitney U-test, the two-sample Kolmogorov-Smirnov test, the two-sample t-test, and the two-sample Cramer-von Mises test. Score-level information fusion is applied and evaluated by: weighted mean, support vector machine, random forest, and likelihood ratio. The accuracy of each comparison/jusion algorithm is evaluated, with results suggesting that, on high resolution eye tracking equipment, it is possible to obtain equal error rates of 16.5% and rank-1 identification rates of 82.6% using the two-sample Cramér-von Mises test and score-level information fusion by random forest, the highest accuracy results on the considered dataset.
    Biometrics (ICB), 2013 International Conference on; 01/2013
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