الفهرس 
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Abstract
Automatic facial emotion recognition (FER) is becoming in more and more
demand in the current days. More industries are trying to incorporate
emotion-aware technologies into their products. Some of those industries,
the automotive industry being one example, require very tight limitations
regarding both run-time and memory footprint of the used models to fit
into small embedded devices. While there is a lot of machine learning and
computer vision work on FER, most of it focuses on obtaining the best possible
system accuracy without being bound by memory constraints. On the
other hand, the work in this thesis explores deep learning models for emotion
recognition in videos for systems with limited memory like robots, cars,
and embedded-systems. Naturally, this comes at the expense of sacrificing
some accuracy. There are two proposed models in this thesis. One is the
mini-xception+LSTM architecure with around 80k parameters. This model
got a validation accuracy of 93% in distinction between Anger and Amusement
emotions on the BioVidEmo dataset. It also achieved 90% validation
accuracy on the CK+ dataset while classifying six emotions as a multi-class
classification problem. The second model, called mini-xception+C3D, had
95k parameters and outperformed the first model. It achieved 94% validation
accuracy on the CK+ dataset. After using a weighted cost function
with the mini-xception+C3D model to handle the class imbalance problem,
validation accuracy increased to 96%, which is a very good result given the
small number of parameters.