Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Second-order statistical measures for text-independent speaker identification
Speech Communication
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Multibiometrics (International Series on Biometrics)
Handbook of Multibiometrics (International Series on Biometrics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fusion strategies in multimodal biometric verification
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Likelihood Ratio-Based Biometric Score Fusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subjective Performance of Texture Based Algorithm for Face Verification: The Role of Databases
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Qualitative Weight Assignment for Multimodal Biometric Fusion
ICAPR '09 Proceedings of the 2009 Seventh International Conference on Advances in Pattern Recognition
A principled approach to score level fusion in multimodal biometric systems
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
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Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Model (GMM) and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle: 1) small size of match scores as is more commonly encountered in biometric fusion, and 2) arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio (LR) method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.