  People represent their emotions in a myriad of ways. Among the most important
ones is whole body expressions which have many applications in different fields
such as human-computer interaction (HCI). One of the most important challenges
in human emotion recognition is that people express the same feeling in various
ways using their face and their body. Recently many methods have tried to
overcome these challenges using Deep Neural Networks (DNNs). However, most of
these methods were based on images or on facial expressions only and did not
consider deformation that may happen in the images such as scaling and rotation
which can adversely affect the recognition accuracy. In this work, motivated by
recent researches on deformable convolutions, we incorporate the deformable
behavior into the core of convolutional long short-term memory (ConvLSTM) to
improve robustness to these deformations in the image and, consequently,
improve its accuracy on the emotion recognition task from videos of arbitrary
length. We did experiments on the GEMEP dataset and achieved state-of-the-art
accuracy of 98.8% on the task of whole human body emotion recognition on the
validation set.
