Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

Correcting Judgment Correctives in National Security Intelligence

Intelligence analysts, like other professionals, form norms that define standards of tradecraft excellence. These norms, however, have evolved in an idiosyncratic manner that reflects the influence of prominent insiders who had keen psychological insights but little appreciation for how to translate those insights into testable hypotheses. The net result is that the prevailing tradecraft norms of best practice are only loosely grounded in the science of judgment and decision-making. The “common sense” of prestigious opinion leaders inside the intelligence community has pre-empted systematic validity testing of the training techniques and judgment aids endorsed by those opinion leaders. Drawing on the scientific literature, we advance hypotheses about how current best practices could well be reducing rather than increasing the quality of analytic products. One set of hypotheses pertain to the failure of tradecraft training to recognize the most basic threat to accuracy: measurement error in the interpretation of the same data and in the communication of interpretations. Another set of hypotheses focuses on the insensitivity of tradecraft training to the risk that issuing broad-brush, one-directional warnings against bias (e.g., over-confidence) will be less likely to encourage self-critical, deliberative cognition than simple response-threshold shifting that yields the mirror-image bias (e.g., under-confidence). Given the magnitude of the consequences of better and worse intelligence analysis flowing to policy-makers, we see a compelling case for greater funding of efforts to test what actually works

Improving the Computation of Brier Scores for Evaluating Expert-Elicited Judgements

Structured expert judgement (SEJ) is a suite of techniques used to elicit expert predictions, e.g. probability predictions of the occurrence of events, for situations in which data are too expensive or impossible to obtain. The quality of expert predictions can be assessed using Brier scores and calibration questions. In practice, these scores are computed from data that may have a correlation structure due to sharing the effects of the same levels of grouping factors of the experimental design. For example, asking common questions from experts may result in correlated probability predictions due to sharing common question effects. Furthermore, experts commonly fail to answer all the needed questions. Here, we focus on (i) improving the computation of standard error estimates of expert Brier scores by using mixed-effects models that support design-based correlation structures of observations, and (ii) imputation of missing probability predictions in computing expert Brier scores to enhance the comparability of the prediction accuracy of experts. We show that the accuracy of estimating standard errors of expert Brier scores can be improved by incorporating the within-question correlations due to asking common questions. We recommend the use of multiple imputation to correct for missing data in expert elicitation exercises. We also discuss the implications of adopting a formal experimental design approach for SEJ exercises

On Classification in Human-driven and Data-driven Systems

Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of machine learning techniques. Despite the significant efforts devoted to training and feature selection in classification systems, misclassifications do occur and their effects can be critical in various applications. The central goal of this thesis is to analyze classification problems in human-driven and data-driven systems, with potentially unreliable components and design effective strategies to ensure reliable and effective classification algorithms in such systems. The components/agents in the system can be machines and/or humans. The system components can be unreliable due to a variety of reasons such as faulty machines, security attacks causing machines to send falsified information, unskilled human workers sending imperfect information, or human workers providing random responses. This thesis first quantifies the effect of such unreliable agents on the classification performance of the systems and then designs schemes that mitigate misclassifications and their effects by adapting the behavior of the classifier on samples from machines and/or humans and ensure an effective and reliable overall classification. In the first part of this thesis, we study the case when only humans are present in the systems, and consider crowdsourcing systems. Human workers in crowdsourcing systems observe the data and respond individually by providing label related information to a fusion center in a distributed manner. In such systems, we consider the presence of unskilled human workers where they have a reject option so that they may choose not to provide information regarding the label of the data. To maximize the classification performance at the fusion center, an optimal aggregation rule is proposed to fuse the human workers\u27 responses in a weighted majority voting manner. Next, the presence of unreliable human workers, referred to as spammers, is considered. Spammers are human workers that provide random guesses regarding the data label information to the fusion center in crowdsourcing systems. The effect of spammers on the overall classification performance is characterized when the spammers can strategically respond to maximize their reward in reward-based crowdsourcing systems. For such systems, an optimal aggregation rule is proposed by adapting the classifier based on the responses from the workers. The next line of human-driven classification is considered in the context of social networks. The classification problem is studied to classify a human whether he/she is influential or not in propagating information in social networks. Since the knowledge of social network structures is not always available, the influential agent classification problem without knowing the social network structure is studied. A multi-task low rank linear influence model is proposed to exploit the relationships between different information topics. The proposed approach can simultaneously predict the volume of information diffusion for each topic and automatically classify the influential nodes for each topic. In the third part of the thesis, a data-driven decentralized classification framework is developed where machines interact with each other to perform complex classification tasks. However, the machines in the system can be unreliable due to a variety of reasons such as noise, faults and attacks. Providing erroneous updates leads the classification process in a wrong direction, and degrades the performance of decentralized classification algorithms. First, the effect of erroneous updates on the convergence of the classification algorithm is analyzed, and it is shown that the algorithm linearly converges to a neighborhood of the optimal classification solution. Next, guidelines are provided for network design to achieve faster convergence. Finally, to mitigate the impact of unreliable machines, a robust variant of ADMM is proposed, and its resilience to unreliable machines is shown with an exact convergence to the optimal classification result. The final part of research in this thesis considers machine-only data-driven classification problems. First, the fundamentals of classification are studied in an information theoretic framework. We investigate the nonparametric classification problem for arbitrary unknown composite distributions in the asymptotic regime where both the sample size and the number of classes grow exponentially large. The notion of discrimination capacity is introduced, which captures the largest exponential growth rate of the number of classes relative to the samples size so that there exists a test with asymptotically vanishing probability of error. Error exponent analysis using the maximum mean discrepancy is provided and the discrimination rate, i.e., lower bound on the discrimination capacity is characterized. Furthermore, an upper bound on the discrimination capacity based on Fano\u27s inequality is developed