  Facial emotion recognition is a vast and complex problem space within the
domain of computer vision and thus requires a universally accepted baseline
method with which to evaluate proposed models. While test datasets have served
this purpose in the academic sphere real world application and testing of such
models lacks any real comparison. Therefore we propose a framework in which
models developed for FER can be compared and contrasted against one another in
a constant standardized fashion. A lightweight convolutional neural network is
trained on the AffectNet dataset a large variable dataset for facial emotion
recognition and a web application is developed and deployed with our proposed
framework as a proof of concept. The CNN is embedded into our application and
is capable of instant real time facial emotion recognition. When tested on the
AffectNet test set this model achieves high accuracy for emotion classification
of eight different emotions. Using our framework the validity of this model and
others can be properly tested by evaluating a model efficacy not only based on
its accuracy on a sample test dataset, but also on in the wild experiments.
Additionally, our application is built with the ability to save and store any
image captured or uploaded to it for emotion recognition, allowing for the
curation of more quality and diverse facial emotion recognition datasets.
