Facial expression recognition using adapted residual based deep neural network

Emotion on our face can determine our feelings, mental state and can directly impact our decisions. Humans are subjected to undergo an emotional change in relation to their living environment and or at a present circumstance. These emotions can be anger, disgust, fear, sadness, happiness, surprise or neutral. Due to the intricacy and nuance of facial expressions and their relationship to emotions, accurate facial expression identification remains a difficult undertaking. As a result, we provide an end-to-end system that uses residual blocks to identify emotions and improve accuracy in this research field. After receiving a facial image, the framework returns its emotional state. The accuracy obtained on the test set of FERGIT dataset (an extension of the FER2013 dataset with 49300 images) was 75%. This proves the efficiency of the model in classifying facial emotions as this database poses a bunch of challenges such as imbalanced data, intraclass variance, and occlusion. To ensure the performance of our model, we also tested it on the CK+ database and its output accuracy was 97% on the test set