The present paper is Part 2 in this series of two papers. In Part 1 we provided an introduction
to Multiple Classifier Systems (MCS) with a focus into the fundamentals: basic nomenclature …

We provide an introduction to Multiple Classifier Systems (MCS) including basic
nomenclature and describing key elements: classifier dependencies, type of classifier …

ABSTRACT A biometric system is essentially a pattern recognition system that operates by
acquiring biometric data from an individual, extracting a feature set from the acquired data …

Unimodal biometric systems have to contend with a variety of problems such as noisy data,
intra-class variations, restricted degrees of freedom, non-universality, spoof attacks, and …

Experimental studies show that multimodal biometric systems for small-scale populations
perform better than single-mode biometric systems. We examine if such techniques scale to …

This paper proposes a unified framework for quality-based fusion of multimodal biometrics.
Quality-dependent fusion algorithms aim to dynamically combine several classifier …

A multimodal biometric system integrates information from multiple biometric sources to
compensate for the limitations in performance of each individual biometric system. We …

The interest on biometric recognition systems for person authentication has experienced an
important growth in the last decade. One of the key factors of this success is the availability …

Biometrics is the science and technology of measuring and analyzing biological data of
human body, extracting a feature set from the acquired data, and comparing this set against …

Multimodal biometric systems consolidate the evidence presented by multiple biometric
sources and typically provide better recognition performance compared to systems based on …