Relational Learning Based Happiness Intensity Analysis in a Group

Pictures and videos from social events and gatherings usually contain multiple people. Physiological and behavioral science studies indicate that there are strong emotional connections among group members. These emotional relations among group members are indispensable to better analyzing individual emotions in a group. However, most of the existing affective computing methods focus on estimating the emotion of a single object only. In this work, we concentrate on estimating happiness intensities of group members while considering the reciprocities among them. We propose a novel facial descriptor that effectively captures happiness related facial action units. We also introduce two different structural regression models, Continuous Conditional Random Fields (CCRF) and Continuous Conditional Neural Fields (CCNF), for estimating emotions of group members. Our experimental results on HAPPEI dataset demonstrate the viability of proposed features and the two frameworks.