Earticle

초록

영어

This paper describes a combined behavioral techniques based on speech and signature biometrics modalities. Fusion of multiple biometric modalities for human verification performance improvement has received considerable attention. 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. Soft decision level fusion based Gaussian mixture models (GMM), in which the (EM) and (GEM) algorithms for estimating the parameters of the mixture model and the number of mixture components have been compared. The test performance of the fusion, EER=0.0 % for "EM" and EER=0.02 % for "GEM", show that the combined behavioral information scheme is more robust and have a discriminating power, which can be explored for identity authentication.

목차

Abstract
 1. Introduction
 2. Verification Traits
  2.1 Speech Analysis and Feature Extraction
  2.2 Signature Verification Systems
 3. Multimodal Biometric Decision Fusion Methods
  3.1 Theoretical Analysis for Decision Level Fusion
  3.2 Soft Decision Level Fusion
  3.3 Maximum Likelihood Parameter Estimation
 4. Experiments and Results
  4.1 Performance Criteria
 5. Conclusion
 References

키워드

저자

• Mohamed Soltane [ Electrical Engineering & Computing Department, Faculty of Sciences & Technology Doctor Yahia Fares University OF Medea ]
• Mimen Bakhti [ Electrical Engineering & Computing Department, Faculty of Sciences & Technology Doctor Yahia Fares University OF Medea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605