Earticle

초록

영어

In this paper, the use of finite Gaussian mixture modal (GMM) based Expectation Maximization (EM) estimated algorithm for score level data fusion is proposed. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in mono modal biometric systems. Simulation show that finite mixture modal (GMM) is quite effective in modelling the genuine and impostor score densities, fusion based the resulting density estimates achieves a significant performance on eNTERFACE 2005 multi-biometric database based on dynamic face and signature modalities.

목차

Abstract
 1. Introduction
 2. Authentication Traits
  2.1 Face Extraction and Recognition
  2.2 Signature Verification Systems
 3. Multimodal Biometric Fusion Decision
  3.1 Adaptive Bayesian Method Based Score Fusion
 4. Experiments and Results
 5. Discussion and Conclusions
 References

키워드

저자

• Soltane Mohamed [ Electrical Engineering & Computing Department, Faculty of Sciences & Technology Doctor yahia fares University OF MEDEA, 26000 MEDEA, ALGERIA & Laboratoire des Systèmes Électroniques Avancées (LSEA) ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605