An Analysis of Minutiae Matching Strength

By Nalini K. Ratha, J. Connell, Ruud M. Bolle. 2001

488 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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Citations in the corpus

Deep Representations for Iris, Face, and Fingerprint Spoofing Detection (2014, background): “echniques have been created to defeat such biometric systems. There are several ways to spoof a biometric system [2], [3]. Indeed, previous studies show at least eight different points of attack [4], [5] that can be divided into two main groups: direct and indirect attacks. The former considers the ”
Guide to Biometrics (2003, background): “More on this subject can be found in [179,181].”
On the vulnerability of palm vein recognition to spoofing attacks (2015, background): “These vulnerable points can be broadly divided into two main groups [6]: i) direct attacks, where the sensor is attacked using synthetic biometric samples without specific knowledge about the system, and ii) indirect attacks, where the intruder needs to have some additional information about the int”
Biometric Template Security (2008, background): “Figure 5: Points of attack in a generic biometric system (adapted from [13]).”
Adversarial Biometric Recognition : A review on biometric system security from the adversarial machine-learning perspective (2015, background): “Different vulnerabilities of biometric systems, specific attacks that can exploit them, and corresponding countermeasures have been analyzed in the literature [7], [8] and in research projects.”
Face Anti-spoofing: Visual Approach (2014, background): “1 Possible attack points in a generic biometric system (from [10])”
Pattern Recognition Systems under Attack: Design Issues and Research Challenges (2014, methodology): “When online learning is used, the learning algorithm is periodically rerun on novel data during operation.”
Toward an attack-sensitive tamper-resistant biometric recognition with a symmetric matcher: A fingerprint case study (2014, background): “, in [1] and [2] which advocate the need to protect the biometric template, the sensor, and all components of the system.”
An evaluation of indirect attacks and countermeasures in fingerprint verification systems (2011, background): “Ratha et al. (2001) have studied and systematically categorized in eight classes the possible types of attacks.”
Face Recognition on Consumer Devices: Reflections on Replay Attacks (2015, methodology): “One technique to validate the authenticity of information is to include a form of digital watermark [5], [6].”
Biometrics: a tool for information security (2006, background): “Vulnerabilities in a biometric system (adapted from [16]).”
Attacks on biometric systems: a case study in fingerprints (2004, background): “Eight different attack points in a biometric authentication system (adapted from [2]). biometric data) can be bombarded with many bogus access requests, to a point where the server’s computational resources cannot handle valid requests any more.”
A survey on biometric cryptosystems and cancelable biometrics (2011, methodology): “Moreover, most of these schemes have been found to suffer from serious security vulnerabilities [8,190].”
On the vulnerability of face verification systems to hill-climbing attacks (2010, background): “In [4] Ratha identified and classified eight possible attack points for biometric recognition systems.”
Security evaluation of biometric authentication systems under real spoofing attacks (2012, background): “They are mostly referred to as spoofing attacks [27, 32, 16, 14, 6].”
Fingerprint-Based Fuzzy Vault: Implementation and Performance (2007, background): “For example, factors, such as translation, rotation, nonlinear distortion, skin conditions, and noise lead to intraclass variations in fingerprints [6] (see Fig.”