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A failure detector is a fundamental abstraction in distributed computing. This paper surveys this abstraction through two dimensions. First we study failure detectors as building blocks to simplify the design of reliable distributed algorithms. In particul ...
Gabor features have been extensively used for facial image analysis due to their powerful representation capabilities. This paper focuses on selecting and combining multiple Gabor classifiers that are trained on, for example, different scales and local regi ...
Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...
The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as cascade), the scan ...
Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has bee ...
In this paper we propose a novel information theoretic criterion for optimizing the linear combination of classifiers in multi stream automatic speech recognition. We discuss an objective function that achieves a trade-off between the minimization of a bou ...
Classifiers based on Gaussian mixture models are good performers in many pattern recognition tasks. Unlike decision trees, they can be described as stable classifier: a small change in the sampling of the training set will produce not a large change in the ...
Combining several classifiers has become a very active subdiscipline in the field of pattern recognition. For years, pattern recognition community has focused on seeking optimal learning algorithms able to produce very accurate classifiers. However, empiri ...
We investigate the invariance of posterior features estimated using MLP trained on auxiliary corpus towards different data condition and different distance measures for matching posterior features in the context of template-based ASR. Through ASR studies o ...
Fisher kernels combine the powers of discriminative and generative classifiers by mapping the variable-length sequences to a new fixed length feature space, called the Fisher score space. The mapping is based on a single generative model and the classifier ...
