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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    • "Initially, the synthesis of biometric data emerges as a solution to the lack of large scale biometric databases. Additionally, synthetic databases have several applications such as performance evaluation (Cappelli et al., 2006; Maltoni et al. 2009), security assessment (Gomez-Barrero et al., 2014), modelling (Plamondon et al., 2014) or improving enrollment (Galbally et al., 2009). "
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    ABSTRACT: This work proposes and analyzes a novel methodology for hand-shape image synthesis. The hand-shape is a popular biometric trait with a high convenience of use and non-intrusive acquisition. The proposed algorithm allows to generate realistic images with natural intra-person and inter-person variability. The method is based on the Active Shape Model algorithm which has been modified in order to add the biometric information typical of new synthetic identities. The generated images are evaluated using three public databases and two hand-shape recognition systems. The results show the suitability of the synthetic data for biometric recognition works. In addition, two novel applications have been proposed to provide new insights in hand-shape biometric recognition including: improvement of machine learning classification based on synthetic training sets and scalability analysis of hand-shape biometrics when the population of the database is increased by two orders of magnitude with respect to existing databases.
    Pattern Recognition Letters 01/2016; 68. DOI:10.1016/j.patrec.2015.09.011 · 1.55 Impact Factor
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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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    ABSTRACT: Automatic latent fingerprint identification is a useful tool for criminal investigation. However, the accuracy of identification reported in the state-of-the-art literature is low due to the distortion in latent fingerprint images. In this paper, we describe a new algorithm based on the use of clustering which is independent of the minutiae descriptors. The proposed technique improves the robustness of identification in the presence of large non-linear deformation which is associated with latent fingerprint images. The new algorithm finds multiple overlapping clusters of matching minutiae pairs which are merged together to find matching minutiae. Several experiments performed using latent fingerprint databases show that our proposed algorithm achieves higher accuracy than those presented in state-of-the-art literature. Moreover, the results show that the proposed algorithm is successful in dealing with the large distortion associated with latent fingerprints formed under the worst conditions.
    Neurocomputing 12/2015; In Press. DOI:10.1016/j.neucom.2015.05.130 · 2.08 Impact Factor
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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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