Alone versus in-a-group: A multi-modal framework for automatic affect recognition

Abstract

Recognition and analysis of human affect has been researched extensively within the field of computer science in the last two decades. However, most of the past research in automatic analysis of human affect has focused on the recognition of affect displayed by people in individual seeings and little attention has been paid to the analysis of the affect expressed in group settings. In this paper, we first analyze the affect expressed by each individual in terms of arousal and valence dimensions in both individual and group videos and then propose methods to recognize the contextual information, i.e., whether a person is alone or in-a-group by analyzing their face and body behavioral cues. For affect analysis, we first devise affect recognition models separately in individual and group videos and then introduce a cross-condition affect recognition model that is trained by combining the two different types of data. We conduct a set of experiments on two datasets that contain both individual and group videos. Our experiments show that (1) the proposed Volume Quantized Local Zernike Moments Fisher Vector (vQLZM-FV) outperforms other unimodal features in affect analysis; (2) the temporal learning model, Long-Short Term Memory Networks (LSTM), works better than the static learning model, Support Vector Machine (SVM); (3) decision fusion helps to improve the affect recognition, indicating that body behaviors carry emotional information that is complementary rather than redundant to the emotion content in facial behaviors; and (4) it is possible to predict the context, i.e., whether a person is alone or in-a-group, using their non-verbal behavioral cues.