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.

Investigation of Automatic Video Summarization using Viewerxe2x80x99s Physiological, Facial and Attentional Features Video summarization aims at the selection of a concise and representative set of keyframes or video segments that allows the identification of the video content. In either cases, traditional summarization techniques usually work by segmenting the video into shots, representing video frames as feature vectors of color, texture, audio, among other features, clustering frames with similar features and selecting most representative keyframes or segments, sometimes guided by a videotosummary ratio target. The resulting summaries are typically subject independent and do not take into account specific viewerxe2x80x99s behavior. Instead of using intrinsic features extracted from the video for summarization, in this article we study whether personalized (subject dependent) video summaries can be obtained from physiological, facial, and attentional data captured from the viewers. More specifically, we study the relationship between personalized video summaries reported by viewers and their data captured during the display of different video genres. A dataset of fifteen videos was used in the experiments. During the exhibition of the videos, the viewerxe2x80x99s physiological, facial, and attentional data were recorded, analyzed and synchronized. Several machine learning models were trained to test our hypothesis. We obtained kfold cross validation accuracies that were above the chance for the best learned models. As a result of this study, we conclude that it is possible to train a learning machine that can produce customized summaries that are closer to user preferences compared to randomly produced summaries.