Gabor LBP Research Articles

Facial Expression Recognition using Hybrid Texture Features based Ensemble Classifier

Communication is fundamental to humans. In the literature, it has been shown through many scientific research studies that human communication ranges from 54 to 94 percent is non-verbal. Facial expressions are the most of the important part of the non-verbal communication and it is the most promising way for people to communicate their feelings and emotions to represent their intentions. Pervasive computing and ambient intelligence is required to develop human-centered systems that actively react to complex human communication happening naturally. Therefore, Facial Expression Recognition (FER) system is required that can be used for such type of problem. In this paper, FER system has been proposed by using hybrid texture features to predict the expressions of human. Existing FER system has a problem that these systems show discrepancies in different cultures and ethnicities. Proposed systems also solve this type of problem by using hybrid texture features which are invariant to scale as well as rotate. For texture features, Gabor LBP (GLBP) features have been used to classify expressions by using Random Forest Classifier. Experimentation has been performed on different facial databases that demonstrate promising results.

Gabor-LBP Features and Combined Classifiers for Music Genre Classification

In this paper, we propose the combination of two visual features with the Gabor filters and LBP for music genre classification. In addition, we use combined classifiers training and testing data to reach a final decision. The experiment shows that we not only reduce the amount of time extracting features, but also achieve a satisfactory result.