Role of Group Level Affect to Find the Most Influential Person in Images

Group affect analysis is an important cue for predicting various group traits. Generally, the estimation of the group affect, emotional responses, eye gaze and position of people in images are the important cues to identify an important person from a group of people. The main focus of this paper is to explore the importance of group affect in finding the representative of a group. We call that person the “Most Influential Person” (for the first impression) or “leader” of a group. In order to identify the main visual cues for “Most Influential Person”, we conducted a user survey. Based on the survey statistics, we annotate the “influential persons” in 1000 images of Group AFfect database (GAF 2.0) via LabelMe toolbox and propose the “GAF-personage database”. In order to identify “Most Influential Person”, we proposed a DNN based Multiple Instance Learning (Deep MIL) method which takes deep facial features as input. To leverage the deep facial features, we first predict the individual emotion probabilities via CapsNet and rank the detected faces on the basis of it. Then, we extract deep facial features of the top-3 faces via VGG-16 network. Our method performs better than maximum facial area and saliency-based importance methods and achieves the human-level perception of “Most Influential Person” at group-level.