Recognizing Perceived Emotions from Facial Expressions

Expression recognition has seen an increase in research in past years, however, little work has been on recognizing perceived emotion (i.e. subject self-reporting of emotion). Considering this, we investigate the perceived emotion of subjects that perform tasks meant to elicit emotion. To facilitate this investigation, we use the BP4D+ multimodal spontaneous emotion corpus. We first statistically analyze the subject's perceived emotions across 10 tasks available in BP4D+. We show the percentage of subjects that felt specific emotions for each of the tasks. This is done across all tested subjects, as well as male and female subjects independently. Along with our statistical analysis, we also propose a 3D convolutional neural network (CNN) architecture to recognize multiple emotions felt for each task sequence. We report accuracy, F1-binary and AUC for all subjects, as well as male and female subjects.