Face Behavior Recognition Through Support Vector Machines

Abstract

Abstract-Communication between computers and humans has grown to be a major field of research. Facial Behavior Recognition through computer algorithms is a motivating and difficult field of research for establishing emotional interactions between humans and computers. Although researchers have suggested numerous methods of emotion recognition within the literature of this field, as yet, these research works have mainly focused on one method for their system output i.e. used one facial database for assessing their works. This may diminish the generalization method and additionally it might shrink the comparability range. A proposed technique for recognizing emotional expressions that are expressed through facial aspects of still images is presented. This technique uses the Support Vector Machines (SVM) as a classifier of emotions. Substantive problems are considered such as diversity in facial databases, the samples included in each database, the number of facial expressions experienced an accurate method of extracting facial features, and the variety of structural models. After many experiments and the results of different models being compared, it is determined that this approach produces high recognition rates.

Cross-dataset emotion recognition from facial expressions through convolutional neural networks

“…Ahmed et al [2], in turn, divided the face into three regions of interest (eyes region, mouth regions and a variable auxiliary region) and tracked fiducial points from these regions to generate shape features based on the Euclidean distance between each pair of points. Emotion recognition was carried out by an SVM classifier.…”