Statistical Models for Assessing the Individuality of Fingerprints

Dass, Sarat C.; Zhu, Yanming; Jain, Anil K.

doi:10.1109/autoid.2005.46

Cited by 18 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 2 shows that more the number of minutiae in the template and the input, the higher the PRC. In experiments conducted on the FVC2002 DB1, there are some differences between the results obtained here and the results in Jain et al [9]. This may result from use of different matching algorithms, which w 0 depends on.…”

Section: Experiments and Resultscontrasting

confidence: 54%

“…This is a much stronger result than without using ridge information which is 1 in 200,000. With 20 matching minutiae this probability is one in 300 trillion, as opposed to the earlier result of 1 in 100 million in [9]. Since proposed ridge information model offers a more reasonable and more accurate fingerprint representation, PRC values with ridge information are much smaller than PRC values without ridge information.…”

Section: Discussionmentioning

confidence: 59%

“…Thus, there are a total of 800 fingerprints using which the model has been developed. We compare the results to that of [9]. Random fingerprints are generated from the model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The latest class of models, namely, the generative models aim at being flexible to represent observed distributions through different fingerprint databases and then ascertained uncertainties from models. Based on the the assumed non-independence of minutia locations and orientations, various mixture models could be used [8] and [9]. In existing generative models only minutiae have been modeled without considering ridge features.…”

Section: Generative Models For Fingerprint Individualitymentioning

confidence: 99%

See 3 more Smart Citations

Computational Methods for Determining Individuality

Srihari

Computational Forensics

View full text Add to dashboard Cite

Abstract. Individuality is the state or quality of being an individual. We establish a computational methodology to determine whether a particular modality of data is sufficient to establish the individuality of every individual or even a demographic group. To test the individuality, generative models are given or learned to represent the distribution of certain characteristics such as birthday, human heights and fingerprints. Given the individuality assessments of different characteristic, the models based on multiple characteristics are proven to get strengthen individuality.

show abstract

Section: Experiments and Resultscontrasting

confidence: 54%

Section: Discussionmentioning

confidence: 59%

“…Thus, there are a total of 800 fingerprints using which the model has been developed. We compare the results to that of [9]. Random fingerprints are generated from the model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Generative Models For Fingerprint Individualitymentioning

confidence: 99%

See 2 more Smart Citations

Computational Methods for Determining Individuality

Srihari

Computational Forensics

View full text Add to dashboard Cite

show abstract

“…But a good model would be a method of gaining insights into the fundamental accuracy bounds of the measurement; more typically, these models may be relatively less effective in predicting performance. In the discriminative approach, definition of decision thresholds based on minimizing risk is an issue [9]. Discriminative methods lead to higher performing automated systems, but do not readily lead to fundamental understanding of underlying issues [10].…”

Section: Introductionmentioning

confidence: 99%

Generative Models and Probability Evaluation for Forensic Evidence

Srihari

2011

Pattern Recognition, Machine Intelligence and Biometrics

View full text Add to dashboard Cite

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.

show abstract