Emotion Recognition In Videos For Low-Memory Systems Using Deep-Learning

Abstract

This paper explores a deep learning model for emotion recognition in videos, suitable for systems with limited memory like robots and embedded-systems. The proposed model is a Mini-xception+LSTM architecure with around 80k parameters. This model got a classification accuracy of 93% in dinstinction between Anger and Amusement emotions using the BioVidEmo dataset, compared to 70% accuracy that a recent work got for the same two emotions, and got 86 % and 90 % classification accuracy using the CK+dataset for seven and six emotions, respectively.