Most of the existing facial expression recognition methods are based on either only texture features or only geometrical features. In this paper, we propose to improve the performance of facial expression recognition by combining both types of features using fuzzy integral. The geometric features used are the displacements of positions of feature points on the face. We first embed them in a low...

Here, we propose a framework that provides a detailed analysis of the spectrotemporal modulations in the acoustic signal, augmented with a discriminative classifier using support vector machines. We have seen that such representation is successful at capturing the nontrivial commonalties within a sound class and differences between different classes[1, 2, 3].

We apply functional approximation techniques to obtain features from online data and use these features to train support vector machines (SVMs) for online mathematical symbol classification. We show experimental results and comparisons with another SVM-based system trained using features used in the literature. The experimental results show that the SVM trained using features from functional ap...

Support vector regression (SVR) has been popular in the past decade, but it provides only an estimated target value instead of predictive probability intervals. Many work have addressed this issue but sometimes the SVR formula must be modified. This paper presents a rather simple and direct approach to construct such intervals. We assume that the conditional distribution of the target value dep...

Remarkable improvements in recognition can be achieved through multibiometric fusion. Among various fusion techniques, score level fusion is the most frequently used in multibiometric system. In this paper, we propose a novel fusion algorithm based on False Reject Rate (FRR) and False Accept Rate (FAR) using Support Vector Machine (SVM). It transfers scores into corresponding FRRs and FARs, thu...

In this paper, we propose to combine an efficient image representation based on local descriptors with a Support Vector Machine classifier in order to perform object categorization. For this purpose, we apply kernels defined on sets of vectors. After testing different combinations of kernel / local descriptors, we have been able to identify a very performant one.

A support vector machine is a new learning machine; it is based on the statistics learning theory and attracts the attention of all researchers. Recently, the support vector machines SVMs have been applied to the problem of financial early-warning prediction Rose, 1999 . The SVMs-based method has been compared with other statistical methods and has shown good results. But the parameters of the ...

This paper proposes a paradigm where commonly made segmental pronunciation errors are modeled as pair-wise confusions between two or more phonemes in the language that is being learnt. The method uses an ensemble of support vector machine classifiers with time varying Mel frequency cepstral features to distinguish between several pairs of phonemes. These classifiers are then applied to classify...

We herein present FactorsR, an RWizard application which provides tools for the identification of the most likely causal factors significantly correlated with species richness, and for depicting on a map the species richness predicted by a Support Vector Machine (SVM) model. As a demonstration of FactorsR, we used an assessment using a database incorporating all species of terrestrial carnivore...

As Internet grows quickly, pornography, which is often printed into a small quantity of publication in the past, becomes one of the highly distributed information over Internet. However, pornography may be harmful to children, and may affect the efficiency of workers. In this paper, we design an easy scheme for detecting pornography. We exploit primitive information from pornography and use thi...