Abstract
Cohort Information, User-Specific parameters and Quality Measures are the three sources of information that can be used to improve the performance of uni-modal and multi-modal biometric authentication. In this thesis a novel method for cohort-based normalization is presented. We show that the distribution of scores produced by cohort models ordered with respect to their similarity to the template show a discriminative pattern for genuine and impostor claims. Using this novel finding, we propose to model the cohort scores profile as a polynomial function of rank order. The polynomial coefficients fitted through cohort scores are used as features to combine with the raw score using a machine learning-based approach. Experimental results show the superior performance of the proposed cohort-based normalization method with respect to the state of art cohort normalization methods. Based on the theory developed in the thesis, explaining the variance of the coefficients of a line fitted through cohort scores as a function of the rank order, we propose a strategy for selecting a subset of cohort models in order to reduce the computational complexity of polynomial regression-based normalization. We show that by including cohort models of the least and highest rank order, the performance of the polynomial regression-based cohort normalization is improved. This thesis investigates the merit of different combinations of the aforementioned information sources in uni-modal and multi-modal biometric systems. We show the performance of a combination of any two information sources is better than that of using one of them alone. We also show that the performance of combining all three information sources is better than that of any combination of two information sources. We propose two frameworks for combining information sources in multi-modal fusion: (1) Joint Fusion (2) Naive Bayesian Fusion. The Naive Bayesian fusion is derived using the assumption of independence between expert outputs as well as information sources. We also show that the Naive Bayesian fusion outperforms the Joint fusion in all combinations. The difference between these two strategies becomes more significant when the number of experts involved in the fusion increases.