  Facial expressions are the most common universal forms of body language. In
the past few years, automatic facial expression recognition (FER) has been an
active field of research. However, it is still a challenging task due to
different uncertainties and complications. Nevertheless, efficiency and
performance are yet essential aspects for building robust systems. We proposed
two models, EmoXNet which is an ensemble learning technique for learning
convoluted facial representations, and EmoXNetLite which is a distillation
technique that is useful for transferring the knowledge from our ensemble model
to an efficient deep neural network using label-smoothen soft labels for able
to effectively detect expressions in real-time. Both of the techniques
performed quite well, where the ensemble model (EmoXNet) helped to achieve
85.07% test accuracy on FER2013 with FER+ annotations and 86.25% test accuracy
on RAF-DB. Moreover, the distilled model (EmoXNetLite) showed 82.07% test
accuracy on FER2013 with FER+ annotations and 81.78% test accuracy on RAF-DB.
Results show that our models seem to generalize well on new data and are
learned to focus on relevant facial representations for expressions
recognition.
