Measurement of Fingerprint Individuality

Stoney, David A.

doi:10.1201/9781420041347.ch9

Cited by 42 publications

(38 citation statements)

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“…We can see that more minutiae the template and input fingerprint have, higher the PRC is. In experiments conducted on the NIST 4 dataset, the PRC values obtain here are smaller than the results in [22,24]. The differences mainly result from use of differnt ways to evaluate PRC from gererative models which is described in Section 21.5.4.…”

Section: Evaluation With Fingerprint Databasescontrasting

confidence: 44%

Generative Models and Probability Evaluation for Forensic Evidence

Srihari

2011

Pattern Recognition, Machine Intelligence and Biometrics

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Generative approaches to pattern recognition and machine learning involve two aparts: first describing the underlying probability distributions and then using such models to compute probabilities or make classificatory decisions. We consider generative models for forensic evidence where the goal is to describe the distributions using graphical models and to use such models to compute probabilistic metrics for measuring the degree of individuality of a forensic modality or of a piece of evidence. The metrics are defined as variations of the probability of random correspondence (PRC) when evidence consists of a set of measurements and correspondence is within a tolerance. Three metrics are defined, the first two of which concern the modality and the third concerns evidence: 1) PRC of two samples, 2) PRC among a random set of n samples (nPRC), and 3) PRC between a specific sample among n others (specific nPRC). Computation of these probabilities are described using graphical models which makes all the variables explicit. The metrics are evaluated for several cases-some of which are illustrative (birthdays and heights) and others concern fingerprints. For birthdays, which are discretevalued and exact, assuming uniformly distributed birthdays, while nPRC rapidly approaches unity with higher n (which is the well-known birthday paradox), specific nPRC grows much less rapidly. For human heights, which are continuous-valued scalars, a quantization is needed and results are genderspecific: assuming Gaussian distributed heights, the PRC for males is higher than for females due to lower variance. Two forms of fingerprint representation are considered: ridge flow and minutiae. Gaussian mixtures are used to model location and orientation of minutiae. With parameters estimated from standard databases (using expectation maximization), fingerprint PRCs are determined for given numbers of available and matching minutiae (which correspond to quantization), for the case of 36 available minutiae where 24 of them match, the PRC is 4.0 × 10 −34 . The methodology put forward should be value to establish the relative value of different types of forensic evidence in courtroom scenarios.

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Section: Evaluation With Fingerprint Databasescontrasting

confidence: 44%

Generative Models and Probability Evaluation for Forensic Evidence

Srihari

2011

Pattern Recognition, Machine Intelligence and Biometrics

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“…The authors in [14] report the value of for fingerprint images containing 12, 26, 36, and 46 minutiae (Table III). While the individuality of the minutiae-based fingerprint representation based on their model is lower than other estimates in the literature (e.g., [61]), their results indicate that the likelihood of an adversary guessing someone's fingerprint pattern (e.g., requiring matching 20 or more minutia from a total of 36) is significantly lower than a hacker being able to guess a six-character alphanumerical case-sensitive (most probably weak) password by social engineering techniques (most common passwords are based on birthday, spouse's name, etc.) or by brute force (the probability of guessing such a password by brute force is ).…”

Section: A Zero-effort Attacksmentioning

confidence: 71%

Biometrics: A Tool for Information Security

Jain

Ross

Pankanti³

2006

IEEE Trans.Inform.Forensic Secur.

859

408

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Abstract-Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometrics can solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.

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“…The problem of the uniqueness of fingerprint impressions was earlier addressed [16], and since then, various models have been proposed for establishing this unique identity, a critical analysis of the models proposed has been made by Stoney and Thornton [19], Stoney [20] and Srihari and Srinivasan [21]. These models have been based on very heterogeneous, and generally very small, samples and very disparate approaches (studying different types of minutiae, different fingerprint patterns, different fingers and different finger areas) see [17,18].…”

Section: Introductionmentioning

confidence: 99%