Abstract

Facial expression recognition is a technology that uses biometric features to classify expressions in human faces. This technology plays a significant role in social communication since it conveys a lot of information about people, is considered a sentiment analysis tool, and is able to automatically recognize the seven basic or universal expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. Deep learning methods boost the learning process and facilitate the data creation task. In this work, the proposed approach used a non-classical technique, Inception-Resnet-v2, to pre-trained deep neural networks (DNNs) on more than a million images from the ImageNet and tested utilizing the face expression database from the Cohn-Kanade (CK+). The system had a loss validation of 0.014668% and attained 100% accuracy.

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