Facial Affect Detection using Transfer Learning: A Comparative Study

Facial affect analysis is perceived as one of themost complex and challenging areas for humanisation ofrobots. Several Facial Expression Recognition (FER)systems apply generic machine learning algorithms toextract facial features. This results in an erroneous classification of previously unseen data. This paper improviseson previous research done on emotion detection and im-plements techniques to leverage the potential of Convolu-tional Neural Networks (CNNs) effectively to classify aset of grayscale images of human faces into seven distinctemotion categories. We experiment with some populartransfer learning models to achieve a maximum accuracyof 98% for the seven-class classification task. To eke outfurther precision and reduce the value loss, we incorpo-rate the Squeeze and Excitation Network to theResNet-50 model, which resulted in a validation accuracyof 99.36%.