DeepEmoVision: Unveiling Emotion Dynamics in Video Through Deep Learning Algorithms

Abstract

Emotion detection from videos plays a pivotal role in understanding human behavior and interaction. This study delves into a cutting-edge method that leverages Recurrent Neural Networks (RNN), Support Vector Machines (SVM), K-Nearest Neighbours (KNN), Convolutional Neural Networks (CNN) and to precisely detect emotions exhibited in video content, holding significant importance in comprehending human behavior and interactions. The devised approach entails a multi-phase procedure: initially, employing CNN-based feature extraction to isolate facial expressions and pertinent visual cues by extracting and pre-processing video frames. These extracted features capture intricate patterns and spatial information crucial for discerning emotions. The results of the trials show that CNN, SVM, KNN, and RNN have promising performance, highlighting their potential. Among the other machine learning models, RNN has attained a 95% accuracy rate in recognizing and classifying emotions in video information. This combination of approaches provides a thorough plan for identifying emotions in dynamic visual material in real time.