Investigation on Light-Weight Deep Learning Model for Emotion Recognition Using Facial Expressions

Research findings have unveiled that facial expressions possess the ability to convey a variety of intense emotions. Hence, in this study, a deep-learning based approach, 2-Dimensional Convolutional Neural Network (2D CNN) for facial emotion recognition is proposed. The proposed network is running at least 47.28 times lesser number of parameters at 542,136, compared to the state-of-the-art (SOTA) network from RAVDESS dataset. The saving from reduced parameters is expected to translate into faster execution in real time. The proposed network scored accuracy of 92 ^% and 94% that outperformed majority of the SOTA networks trained on RAVDESS and SAVEE dataset respectively, except one LSTM network from RAVDESS dataset that scored 98.90% in accuracy but with 116.5x higher number of parameters.