Coalitional Tracker for Deception Detection in Thermal Imagery

Abstract

This chapter presents two novel approaches that allow computer vision applications to perform human facial expression recognition (FER). From a prob lem standpoint, we focus on FER beyond the human visual spectrum, in long-wave infrared imagery, thus allowing us to offer illumination-independent solutions to this important human-computer interaction problem. From a methodological stand point, we introduce two different feature extraction techniques: a principal com ponent analysis-based approach with automatic feature selection and one based on texture information selected by an evolutionary algorithm. In the former, facial fea tures are selected based on interest point clusters, and classification is carried out us ing eigenfeature information; in the latter, an evolutionary-based learning algorithm searches for optimal regions of interest and texture features based on classification accuracy. Both of these approaches use a support vector machine-committee for classification. Results show effective performance for both techniques, from which we can conclude that thermal imagery contains worthwhile information for the FER problem beyond the human visual spectrum.