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Biometric authentication is a process of verifying an identity claim using a person's behavioral and physiological characteristics. Due to vulnerability of the system to environmental noise and variation caused by the user, fusion of several biometric-enab ...
Multi-stream based automatic speech recognition (ASR) systems outperform their single stream counterparts, especially in the case of noisy speech. However, the main issues in multi-stream systems are to know a) Which streams to be combined, and b) How to c ...
In this paper, we present a system for image replica detection. More specifically, the technique is based on the extraction of 162 features corresponding to texture, color and gray-level characteristics. These features are then weighted and statistically n ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Classifier performance is often enhanced through combining multiple streams of information. In the context of multi-stream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution outp ...
Full-combination multi-band approach has been proposed in the literature and performs well for band-limited noise. But the approach fails to deliver in case of wide-band noise. To overcome this, multi-stream approaches are proposed in literature with varyi ...
The signal processing community is increasingly interested in using information theoretic concepts to build signal processing algorithms for a variety of applications. A general theory on how to apply the mathematical concepts of information theory to the ...
In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
Boosting is a general method for training an ensemble of classifiers with a view to improving performance relative to that of a single classifier. While the original AdaBoost algorithm has been defined for classification tasks, the current work examines it ...
