Facial expressions are the most  universal forms of body language and automatic facial expression recognition is one of the challenging tasks due to different uncertainties. However, it has been an active field of research for many years. 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  .
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. In this work, we propose two models named EmoXNet and EmoXNetLite. EmoXNet is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time. Both models attained better accuracy level in comparison to the models reported to date. The ensemble model (EmoXNet) attained 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB ). Whereas , the distilled model (EmoXNetLite) attained 82.07\% test accuracy on FER-2013 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 .
A
common
R
and
. In the past few years,
R
is one of the challenging tasks due to different uncertainties. However, it
(FER)
R
for many years.
. However, it is still a challenging task due to different uncertainties and complications.
R
We proposed two models , EmoXNet which
In this work, we propose two models named EmoXNet and EmoXNetLite. EmoXNet
R
and EmoXNetLite which
whereas EmoXNetLite
D
that is useful
R
for able to effectively detect expressions
to detect expressions effectively
R
of the techniques performed quite well, where the
models attained better accuracy level in comparison to the models reported to date. The
R
helped to achieve
attained
R
FER2013
FER-2013
A
Real-world Affective Faces Database (
R
. Moreover
). Whereas
R
showed
attained
R
FER2013
FER-2013
A
. Results show that our models seem to generalize well on new data and are learned to focus on relevant facial representations for expressions recognition