| Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. Unimodal biometric systems perform person recognition based on a single source of biometric information and are affected by problems like noisy sensor data, non-universality and lack of individuality of the chosen biometric trait, absence of an invariant representation for the biometric trait and susceptibility to circumvention. Some of these problems can be alleviated by using multimodal biometric systems that consolidate evidence from multiple biometric sources. Integration of evidence obtained from multiple cues is a challenging problem and integration at the matching score level is the most common approach because it offers the best trade-off between the information content and the ease in fusion. In this thesis, we address two important issues related to score level fusion. Since the matching scores output by the various modalities are heterogeneous, score normalization is needed to transform these scores into a common domain prior to fusion. We have studied the performance of different normalization techniques and fusion rules using a multimodal biometric system based on face, fingerprint and hand-geometry modalities. The normalization schemes have been evaluated both in terms of their efficiency and robustness to the presence of outliers in the training data. We have also demonstrated how soft biometric attributes like gender, ethnicity, accent and height, that by themselves do not have sufficient discriminative ability to reliably recognize a person, can be used to improve the recognition accuracy of the primary biometric identifiers (e.g., fingerprint and face). We have developed a mathematical model based on Bayesian decision theory for integrating the primary and soft biometric cues at the score level. |
