Deep Learning-based Facial Expression Recognition

This research utilizes several well-known Convolutional Neural Networks (CNNs) for facial expression recognition. By taking advantage of transfer learning, deep networks are able to perform a new classification task with a comparatively smaller training dataset. The experiment was efficiently executed by using these models to classify seven universally recognized emotions, i.e. neutral, happiness, sadness, angry, disgust, fear, and surprise. The models were also fine-tuned using a grid search strategy to identify optimal hyperparameter settings. Evaluated using the CK+ dataset, the transfer learning networks show reasonable performance.