Fusion of Dependent and Independent Biometric Information Sources

Abstract

In this report, an overview of information fusion techniques for dependent and independent sources, specifically for biometric applications, is provided. The information fusion architecture is presented for both dependent and independent sources addressing in detail the various fusion techniques at four different levels namely: raw data or signal level, feature level, decision level and multi-level integrated fusion. Furthermore, the report addresses the question of whether independent biometric sources can be fused to provide multi-modal biometric system with enhanced performance. The report shows that even when the sources are independent, the performance of a multi-modal biometric system can be better than that of a biometric system based on single source. The performance is measured in terms of total false accept rate (FAR) and false rejection rate (FRR). The conditions for achieving an improved performance for the decision level fusion using AND, OK and majority voting are derived theoretically and confirmed through computer simulations.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2005
Accession Number
ADA439652

Entities

People

  • Dongliang Huang
  • Henry Leung
  • Winston Li

Organizations

  • University of Calgary

Tags

Communities of Interest

  • C4I
  • Human Systems
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Authentication
  • Biometric Security
  • Biometrics
  • Computational Science
  • Databases
  • Detectors
  • Feature Extraction
  • Fuzzy Sets
  • Hidden Markov Models
  • Identification
  • Information Science
  • Kalman Filters
  • Machine Learning
  • Neural Networks
  • Particle Swarm Optimization
  • Statistical Estimation

Fields of Study

  • Engineering

Readers

  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.
  • Systems Analysis and Design