Product of Likelihood Ratio Scores Fusion of Dynamic Face and On-line Signature Based Biometrics Verification Application Systems

다국어 초록 (Multilingual Abstract)

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 (Table of Contents)

• 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