Carnegie Mellon University Optimal Classifier Ensembles for Improved Biometric Verification

In practical biometric verification applications, we expect to observe a large variability of biometric data. Single classifiers have insufficient accuracy in such cases. Fusion of multiple classifiers is proposed to improve accuracy. Typically, classifier decisions are fused using a decision fusion rule. Usually, research is done on finding the best decision fusion rule, given the set of classifiers. No account of the decision fusion rule is taken during classifier ensemble generation. By taking into account the decision fusion rule during classifier ensemble generation, the accuracy on decision fusion can be improved. The goal of this thesis research is to generate optimal classifier ensembles. The focus is on ensemble generation rather than the best decision rule evaluation. It has been found in literature that diversity in classifier decisions improves the Majority decision rule accuracy. The first part of the thesis finds the role of diversity on the ensemble accuracy. The ensemble accuracy is equated to the accuracy of the best monotonic decision rule at the given diversity. It is found in this analysis that the And, Or, and Majority decision rules are important. Hence these rules are investigated in detail to find their optimal diversity. The second part of the thesis connects the theory on optimal diversity can be used to generate optimal classifier ensembles in practice. An illustration of the design of multiple classifiers is shown on 2D simulated data. From this, it can be observed how the ensemble design is linked to optimal classifier diversity. It is assumed that the same base classifier is used. The classifiers in the ensemble are different because of training on different subsets of the training set. It is also seen that the data distribution and the base classifier plays a role in the optimal fusion rule as well as the generation of its optimal classifier ensemble. The last part of the thesis applies the learnt guidelines for optimal ensemble generation on real data. The approaches to ensemble design for And, Or and Majority decision rules are demonstrated on the CMU Pose, Illumination and Expression (PIE) face database and the NIST 24 fingerprint database. This shows the applicability of these ideas to general biometric verification problems.