Emotion Detection using Deep Facial Features

Emotion Detection became one of the most efficient and challenging activities in human interactions. In general, facial gestures are normal and clear means of expressing their feelings and intentions to human beings. The main features of non-verbal conversations are facial expressions. The study of the techniques of Face Emotion Recognition involves three key phases, like pre-processing, extracting of features and, classification techniques. This paper proposes the comparison of deep learning architectures available in Keras for emotion detection using the Deep Facial Features in images using Transfer Learning from famous pre-trained models like VGG-16, ResNet152V2, InceptionV3, and Xception and generating bottleneck features for our input images. The performance of these models is evaluated based on the dataset which is a combination of the Cohn-Kanade Dataset (CK+) and Japanese female facial emotion (JAFFE). For the above-mentioned architectures, the accuracies obtained are 83.16 %, 82.15 %, 77.1 %, 78.11 % respectively.