A Color MomentsBased System for Recognition of Emotions Induced by Color Images Images can evoke emotions in the viewers, but are rarely indexed based on their emotional contents because emotions are subjective. Given the ubiquitous use of images, it would therefore be desirable to have an automated classification system for recognition of human emotions induced by the visual content of images (REVC), which can index images based on the emotions that they may induce. The focus of research in REVC systems has been on identification of features that evoke different emotions and their strength. These features are then used to develop REVC systems that can accurately predict emotions, which a given image may evoke. This paper investigates the performance of a color momentbased REVC system, by identifying the suitable color moments, and measuring their effectiveness for emotion recognition. Using a combination of these features, a new REVC system is developed. The proposed method outperforms a commonly used benchmark that relies on more than 100 features to predict emotional content of the image, signifying the fact that using combination of multiple features does not necessarily improve the system performance.

Link between Facial Expressions and Emotional States Induced by Exposure to Multimedia Content The explosive growth of digital videos has created new challenges for computer science. While many advances on video indexing, retrieval and summarization based on general, subjectindependent, objective descriptors have been made in the past years, research on the use of individual subjective preferences and affective states is at the forefront of research and poses great challenges. In this article, we study the relationship between emotional states reported by viewers and their facial physiological changes observed during the display of different video genres. A dataset of twenty videos was created from YouTube video sharing platform. During the exhibition of the videos, the viewerxe2x80x99s facial activities have been recorded and analyzed by means of Action Units (AUs). After that, emotional states selfreported by the viewers were assigned to video shots. Labels were divided into four categories, defined according to a discrete version of Russelxe2x80x99s Circumplex emotion model. Different machine learning models were trained to test the relationship between the measured facial features and the selfreported emotional categories. We obtained kfold cross validation accuracies that were above chance for the best learned models. As a result of this study, we concluded that AUs can indeed be used as an valuable tool to estimate emotional categories during exposure to audiovisual stimuli, and, therefore, should be used in further studies that take advantage of those categories to devise personalized multimedia retrieval and summarization approaches.