Can CNNs Accurately Classify Human Emotions? A Deep-Learning Facial
  Expression Recognition Study
Ashley Jisue Hong David DiStefano Sejal Dua
Emotional Artificial Intelligences are currently one of the most anticipated
developments of AI. If successful, these AIs will be classified as one of the
most complex, intelligent nonhuman entities as they will possess sentience, the
primary factor that distinguishes living humans and mechanical machines. For
AIs to be classified as "emotional," they should be able to empathize with
others and classify their emotions because without such abilities they cannot
normally interact with humans. This study investigates the CNN model's ability
to recognize and classify human facial expressions (positive, neutral,
negative). The CNN model made for this study is programmed in Python and
trained with preprocessed data from the Chicago Face Database. The model is
intentionally designed with less complexity to further investigate its ability.
We hypothesized that the model will perform better than chance (33.3%) in
classifying each emotion class of input data. The model accuracy was tested
with novel images. Accuracy was summarized in a percentage report, comparative
plot, and confusion matrix. Results of this study supported the hypothesis as
the model had 75% accuracy over 10,000 images (data), highlighting the
possibility of AIs that accurately analyze human emotions and the prospect of
viable Emotional AIs.