Recognition of Facial Emotions Using CNN Architecture and FER2013

Humans are social and emotional beings. With the rapid normalization of work-from-home and hybrid work environments, most people are having a difficult time truly expressing their emotions, which when not addressed inevitably leads to drastic mental problems. In order to address or prevent these mental problems, detection and recognition of facial emotions serve as an initial step. This paper presents a facial emotion recognition model working on the CNN (convolutional neural network) architecture, trained and tested using the FER2013 dataset, achieving an accuracy of 74% of detecting seven emotions - Happy, Neutral, Fear, Sadness, Anger, Disgust, and Surprise.