Affective Computing is a research field that addresses this problem and affectively analyze the multimedia to build emotionally intelligent machines, capable of better human machine interaction. The exciting applications of affective computing include affective content recommendation, abstraction and affective description generation. Affective content analysis also helps us find the reason why a specific content is evoking particular emotion in its viewers. For example, image (a) should elicit joyous feeling to most of viewers because of the reason that children are playing and having fun. Similarly, image (b) should induce feelings of fear in its viewers due to presence of scary doll.
Affective Computing at ITU
Much of the difficulty that appears in affective computing is due to presence of “affective gap” which can be defined as the disconnect between low-level visual features (like color, texture and saliency) and high-level affective concepts such as human emotions. Unlike existing work that uses low level visual features we believe that these features are neither sufficient nor adequate enough to model human emotions. Our team focuses on interpretable affective computing and uses high level concepts like objects, places and relationship among them to model human emotions. For example, given images will induce emotions of joy and amusement in the viewers due to presence of high level concepts like sky diving, sea view, park and Halloween.
We aim to extend our study to perform video affective analysis to enable mood based affective retrieval as mood of video or movie is one of the most important factors we consider when we select it for watching. For example, when a user will be sad or tired, he/she would be able to change his/her mood by watching a happy video.
We are conducting a study on analysis of emotions induced in humans when any image is presented to them and collecting a dataset named SentimentMe. Through SentimentMe our aim is to gather user sentiments along with the contextual and content information about the images for better visual sentiment analysis. We want to study the reasons that are behind these induced emotions. You can contribute to this research cause by visiting our data collection page.