Semantic Classifier for Affective Computing

Abstract

One of the most important fields of affective computing is related to the hard problem of emotion recognition. At present, there are several approaches to the problem of automatic emotion recognition based on different methods, like Bayesian classifiers, Support Vector Machines, Linear Discriminant Analysis, Neural Networks or k-Nearest Neighbors, which classify emotions using several features obtained from facial expressions, body gestures, speech or different physiological signals. In this paper, we propose a Semantic Classifier as a new, simple and efficient approach to the problem of automatic emotion recognition. The implementation of the Semantic Classifier is based on the basic, natural principles used to decrease the complexity of problems found in n-dimensional spaces: discretization, structure identification and semantic optimization. The proposed classifier exhibits some self-organizing features and supports learning by repetition, generalization and specialization. It will be used to implement a distributed and robust system for emotion recognition.