  Expressing and identifying emotions through facial and physical expressions
is a significant part of social interaction. Emotion recognition is an
essential task in computer vision due to its various applications and mainly
for allowing a more natural interaction between humans and machines. The common
approaches for emotion recognition focus on analyzing facial expressions and
requires the automatic localization of the face in the image. Although these
methods can correctly classify emotion in controlled scenarios, such techniques
are limited when dealing with unconstrained daily interactions. We propose a
new deep learning approach for emotion recognition based on adaptive multi-cues
that extract information from context and body poses, which humans commonly use
in social interaction and communication. We compare the proposed approach with
the state-of-art approaches in the CAER-S dataset, evaluating different
components in a pipeline that reached an accuracy of 89.30%
