Enhanced deep learning network for emotion recognition from GIF

Abstract

The Graphic Interchange Format (GIF) is a bitmap picture format that has a series of perpetually repeating images or silent movies that may be viewed without the user having to click and start them. GIFs are frequently used to visually represent emotions that are expressed through body language such as gestures, movements, and facial expressions. Computing may be used to recognise thoughts and other emotions like desire, interest, sentiments, etc. by using emotional expressions or movements as face markers or properties in GIFs. The ability to predict emotions in GIFs may make it easier to express oneself on social media and convey a person’s attitude or personality. Emotion detection in GIFs may be utilised for a range of purposes, e.g., developing a recommendation system, detecting inappropriate content, sentiment identification from GIF-induced sentiment as perceived by person and creating a GIF tag generating system. This study discusses the prior contributions made towards emotion identification in GIFs and describes a method for detecting seven different emotion classes (Happy, Anger, Sad, Surprise, Disgust, Fear, and Neutral) in GIFs by combining an activity recognition network with face emotional expression. The suggested deep neural network, RNN, LSTM approach produced an F1-score of 0.89 and an accuracy of 88 percent.