Research Article Open Access

Evaluation on Score Reliability for Biometric Speaker Authentication Systems

Muhammad Hazim Hasan1, Haryati Jaafar1 and Dzati Athiar Ramli1
  • 1 Universiti Sains Malaysia, Malaysia
Journal of Computer Science
Volume 8 No. 9, 2012, 1554-1563

DOI: https://doi.org/10.3844/jcssp.2012.1554.1563

Submitted On: 4 July 2012 Published On: 16 August 2012

How to Cite: Hasan, M. H., Jaafar, H. & Ramli, D. A. (2012). Evaluation on Score Reliability for Biometric Speaker Authentication Systems. Journal of Computer Science, 8(9), 1554-1563. https://doi.org/10.3844/jcssp.2012.1554.1563

Abstract

Problem statement: Fusion weight tuning based on score reliability is imperative in order to ensure the performances of multibiometric systems are sustained. Approach: In this study, two variant of conditions i.e., different performances of individual subsystems and inconsistent quality of test samples are experimented to multibiometric systems. By applying multialgorithm scheme, two types of features extraction method i.e., Linear Predictive Coding (LPC) and Mel Frequency Cepstrum Coefficient (MFCC) are executed in this study. Support Vector Machine (SVM) is used as a classifier for both subsystems for the pattern matching process. Scores from both LPC and MFCC based sub systems are fused at score level fusion using fixed weighting and adaptive weighting approaches. For fixed weighting, sum-rule method is employed while for the adaptive weighting, sum-rule based on weight adaptation and sum-rule with weight produced from fuzzy logic inference are executed. The performances of single, fixed and adaptive systems are then compared. Results: Experimental results show that at 40dB and 20dB SNR signals, EER performances of single systems are 1.1730 and 38.2695% respectively. Consequently, the EER performances are observed as 2.7355 and 1.1359% for the sum-rule based on weight adaptation and sum-rule with weight produced from Fuzzy Logic. Conclusion: The results show that fusion system based on fuzzy logic gives advantage due to its capability in adjusting the weight based on the subsystem performance and quality of the current data.

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Keywords

  • Linear Predictive Coding (LPC)
  • Multialgorithm
  • sum-rule
  • fuzzy logic
  • Mel Frequency Cepstrum Coefficient (MFCC)