Hierarchical mixture models for assessing fingerprint individuality

The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on fingerprint evidence. The main challenge in studies of fingerprint individuality is to adequately capture the variability of fingerprint features in a population. In this paper hierarchical mixture models are introduced to infer the extent of individualization. Hierarchical mixtures utilize complementary aspects of mixtures at different levels of the hierarchy. At the first (top) level, a mixture is used to represent homogeneous groups of fingerprints in the population, whereas at the second level, nested mixtures are used as flexible representations of distributions of features from each fingerprint. Inference for hierarchical mixtures is more challenging since the number of unknown mixture components arise in both the first and second levels of the hierarchy. A Bayesian approach based on reversible jump Markov chain Monte Carlo methodology is developed for the inference of all unknown parameters of hierarchical mixtures. The methodology is illustrated on fingerprint images from the NIST database and is used to make inference on fingerprint individuality estimates from this population.

[1]  L. Wasserman,et al.  Practical Bayesian Density Estimation Using Mixtures of Normals , 1997 .

[2]  Sharath Pankanti,et al.  On the individuality fingerprints , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Lancelot F. James,et al.  Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions , 2001 .

[4]  Stephen P. Brooks,et al.  Convergence Assessment for Reversible Jump MCMC Simulations , 2007 .

[5]  Mike West,et al.  Bayesian Density Estimation and Inference Using , 1994 .

[6]  John I. Thornton,et al.  A Critical Analysis of Quantitative Fingerprint Individuality Models , 1986 .

[7]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[8]  S. Sclove.  The Occurrence of Fingerprint Characteristics as a Two-Dimensional Process , 1979 .

[9]  Anil K. Jain,et al.  Statistical Models for Assessing the Individuality of Fingerprints , 2007, IEEE Trans. Inf. Forensics Secur..

[10]  T. N. Sriram,et al.  Robust estimation of mixture complexity for count data , 2007, Comput. Stat. Data Anal..

[11]  M. Escobar,et al.  Bayesian Density Estimation and Inference Using Mixtures , 1995 .

[12]  Sharath Pankanti,et al.  On the Individuality of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Sarat C. Dass,et al.  A BAYESIAN ANALYSIS OF HIERARCHICAL MIXTURES WITH APPLICATION TO CLUSTERING FINGERPRINTS , 2008 .

[14]  Stephen P. Brooks,et al.  Markov Chain Monte Carlo Convergence Assessment via Two-Way Analysis of Variance , 2000 .