A Review of Facial Emotion Recognition: Comparison of Conventional and Deep Learning Methods

This paper deals with a comparison study including an update and an overview of techniques used for facial emotion expressions. First, we discuss the various models used for facial emotion representation also we describe the process of feature extraction. Along with that, we present Conventional approaches used for facial emotion recognition as well as deep learning approaches. In the paper, we define the advantages of Conventional and Deep Learning approaches and the drawbacks of both approaches. The paper points out also the challenges of both approaches which can be a point of interest for a Ph.D. study.

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Deep Convolutional Neural Network for Robust Facial Emotion Recognition

Emotion and the ability to understand them are considered a channel of non-verbal communication. It is an important factor to achieve a smooth and yet robust interaction between machines and humans. In this paper, we review CNN-based methods for facial emotion recognition and we propose a new cutting edge deep learning approach to classify facial expressions from pictures. To guarantee the efficacy of the method, we used multiple datasets: FER2013, AffectNet, RaFD, and KDEF. We obtained results respectively 82.3%, 76.79%, 78.58 %, and 77.08 %. Those results surpassed the current state of the art. We also compared our achieved measurements to available APIs for facial emotion recognition.