Confidence-Based Rank Level Fusion For Multimodal Biometric Systems
Abstract
In recent years, the inevitable need for reliable biometric identity management systems in applications such as border crossing, welfare distribution, and accessing critical facilities has drawn researchers' attention to the area of biometric. The intrinsic limitations of unimodal biometric systems such as non-universality, sensitivity to noisy sensor data, inter and intra class variations and spoof attacks have resulted in significant attention toward multimodal biometric systems. An important aspect of a multimodal biometric system is the fusion of information from multiple biometric sources. This thesis focuses on using the notion of Resemblance Probability Distributions to calculate confidence measures for different biometric matchers and use these confidence measures in the fusion module to improve the identification rate of the system. This thesis approaches the problem of low inter class variation and low quality data by proposing Rank List Reinforcement and Confidence-based Ranked List Selection methods.
Description
Keywords
Computer Science
Citation
Talebi, H. (2015). Confidence-Based Rank Level Fusion For Multimodal Biometric Systems (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25415