  We present EmotiCon, a learning-based algorithm for context-aware perceived
human emotion recognition from videos and images. Motivated by Frege's Context
Principle from psychology, our approach combines three interpretations of
context for emotion recognition. Our first interpretation is based on using
multiple modalities(e.g. faces and gaits) for emotion recognition. For the
second interpretation, we gather semantic context from the input image and use
a self-attention-based CNN to encode this information. Finally, we use depth
maps to model the third interpretation related to socio-dynamic interactions
and proximity among agents. We demonstrate the efficiency of our network
through experiments on EMOTIC, a benchmark dataset. We report an Average
Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8
over prior methods. We also introduce a new dataset, GroupWalk, which is a
collection of videos captured in multiple real-world settings of people
walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is
also an improvement over prior methods.
