Abstract
Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of group membership – i.e., recognizing which group the individual in question is part of. This paper presents a novel two-phase Support Vector Machine (SVM) based specific recognition model that is learned using an optimized generic recognition model. We conduct a set of experiments using a database collected to study group analysis from multimodal cues while each group (i.e., four participants together) were watching a number of long movie segments. Our experimental results show that the proposed specific recognition model (52%) outperforms the generic recognition model trained across all different videos (35%) and the independent recognition model trained directly on each specific video (33%) using linear SVM.
Highlights
Automatic analysis of a group of people has received much attention in computer vision community for different research purposes
We introduce a novel two-phase Support Vector Machine (SVM) based specific recognition model that is learned using an optimized generic recognition model
3) Experimental results and analysis: The recognition results in terms of recognition accuracy by applying leaveone-subject-out cross-validation are shown in Table II and Table III
Summary
Automatic analysis of a group of people has received much attention in computer vision community for different research purposes. Gallagher et al [1] propose a framework to predict the age and the gender of individuals in group images. Ibrahim et al [2] focus on group activity recognition. Other research fields, including emotion recognition, have started to shift their focus from individual to group settings [3], [4]. Research works focusing on the analysis of social dimensions, such as engagement and rapport in group settings have been introduced [5], [6]. Works focusing on automatic analysis of the relationship between the members of different groups have emerged. In our previous work [8] we introduced group membership recognition using non-verbal behaviors, where group membership recognition refers to recognizing which group each individual is part of
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