Fingerprint identification is based on two basic premises: (i) persistence: the basic characteristics of fingerprints do not change with time; and (ii) individuality: the fingerprint is unique to an individual. The validity of the first premise has been established by the anatomy and morphogenesis of friction ridge skin. While the second premise has been generally accepted to be true based on empirical results, the underlying scientific basis of fingerprint individuality has not been formally tested. As a result, fingerprint evidence is now being challenged in several court cases. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae points to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of falsely associating minutiae-based representations from two arbitrary fingerprints. For example, the probability that a fingerprint with 36 minutiae points will share 15 minutiae points with another arbitrarily chosen fingerprint with 36 minutiae points is 4:26 10 7 . These probability estimates are compared with typical fingerprint matcher accuracy results. Our results show that (i) contrary to the popular belief fingerprint matching is not infallible and leads to some false associations, (ii) the performance of automatic fingerprint matcher does not even come close to the theoretical performance, and (iii) due to the limited information content of the minutiae-based representation, the automatic system designers should explore the use of non-minutiaebased information present in the fingerprints. 1

to be published in the special issue of Computer & Chemistry

of least relative surprise; model checking; change of variable problem; crossvalidation. We consider the problem of deriving Bayesian inference procedures via the concept of relative surprise. The mathematical concept of surprise has been developed by I.J. Good in a long sequence of papers. We make a modiÞcation to this development that permits the avoidance of a serious defect; namely, the change of variable problem. We apply relative surprise to the development of estimation, hypothesis testing and model checking procedures. Important advantages of the relative surprise approach to inference include the lack of dependence on a particular loss function and complete freedom to the statistician in the choice of prior for hypothesis testing problems. Links are established with common Bayesian inference procedures such as highest posterior density regions, modal estimates and Bayes factors. From a practical perspective new inference

Clustering is a very well studied problem that attempts to group similar data points. Most traditional clustering algorithms assume that the data is provided without measurement error. Often, however, real world data sets have such errors and one can obtain estimates of these errors. We present a clustering method that incorporates information contained in these error estimates. We present a new distance function that is based on the distribution of errors in data. Using a Gaussian model for errors, the distance function follows a Chi-Square distribution and is easy to compute. This distance function is used in hierarchical clustering to discover meaningful clusters. The distance function is scale-invariant so that clustering results are independent of units of measuring data. In the special case when the error distribution is the same for each attribute of data points, the rank order of pair-wise distances is the same for our distance function and the Euclidean distance function. The clustering method is applied to the seasonality estimation problem and experimental results are presented for the retail industry data as well as for simulated data, where it outperforms classical clustering methods.

Abstract. Chromosomal aberrations are differences in DNA sequence copy number of chromosome regions 3. These differences may be crucial genetic events in the development and progression of human cancers. Array Comparative Genomic Hybridization is a laboratory method used in cancer research for the measurement of chromosomal aberrations in tumor genomes. A recurrent aberration at a particular genome location may indicate the presence of a tumor suppressor gene or an oncogene. The goal of the analysis of this type of data includes detection of locations of copy number changes, called breakpoints, and estimate of the values of the copy number value before and after a change. Knowing the exact locations of a breakpoint is important to identify possibly damaged genes. This paper introduces genetic local search algorithms to perform this task. 1

Fingerprint Classification and Matching Using a Filterbank By Salil Prabhakar Accurate automatic personal identification is critical in a variety of applications in our electronically interconnected society. Biometrics, which refers to identification based on physical or behavioral characteristics, is being increasingly adopted to provide positive identification with a high degree of confidence. Among all the biometric techniques, fingerprint-based authentication systems have received the most attention because of the long history of fingerprints and their extensive use in forensics. However, the numerous fingerprint systems currently available still do not meet the stringent performance requirements of several important civilian applications. To assess the performance limitations of popular minutiae-based fingerprint verification system, we theoretically estimate the probability of a false correspondence between two fingerprints from di#erent fingers based on the minutiae representation of fingerprints. Due to the limited amount of information present in the minutiae-based representation, it is desirable to explore alternative representations of fingerprints. We present a novel filterbank-based representation of fingerprints. We have used this compact representation for fingerprint classification as well as fingerprint verification. Experimental results show that this algorithm competes well with the state-of-theart minutiae-based matchers. We have developed a decision level information fusion framework which improves the fingerprint verification accuracy when multiple matchers, multiple fingers of the user, or multiple impressions of the same finger are combined. A feature verification and purification scheme is proposed to improve the performance of the minutiae-...

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Multibiometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in unibiometric systems. In this thesis, we address two critical issues in the design of a multibiometric system, namely, fusion methodology and template security. First, we propose a fusion methodology based on the Neyman-Pearson theorem for combination of match scores provided by multiple biometric matchers. The likelihood ratio (LR) test used in the Neyman-Pearson theorem directly maximizes the genuine accept rate (GAR) at any desired false accept rate (FAR). The densities of genuine and impostor match scores needed for the LR test are estimated using finite Gaussian mixture models. We also extend the likelihood ratio based fusion scheme to incorporate the quality of the biometric samples. Further, we also show that the LR framework can be used for designing sequential multibiometric systems by constructing a binary decision tree classifier based on the marginal likelihood ratios of the

This work addresses a central problem of Proteomics: estimating the amounts of each of the thousands of proteins in a cell culture or tissue sample. Although laboratory methods involving isotopes have been developed for this problem, we seek a simpler approach, one that uses more-straightforward laboratory procedures. Specifically, our aim is to use data-mining techniques to infer protein levels from the relatively cheap and abundant data available from high-throughput tandem mass spectrometry (MS/MS). In this thesis, we develop and evaluate several techniques for tackling this problem. Specifically, we develop and evaluate different statistical models of MS/MS data. In addition, to evaluate their biological relevance, we test each method on three real-world datasets generated by MS/MS experiments performed on various tissue samples taken from Mouse.

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