A Comprehensive Evaluation of Emotion Recognition Techniques: Model and Data Analysis

This study evaluates three prominent techniques for emotion recognition: Convolutional Neural Networks (CNNs), the Emotions in Context (EMOTIC) dataset, and feature extraction methods like Principal Component Analysis (PCA) and Local Phase Quantization (LPQ). By analyzing these methods across datasets such as Fuyeor Language (FER) 2013 and EMOTIC, the research highlights CNNs' ability to classify emotions from Electroencephalogram (EEG) signals with high accuracy, enhanced by PCA's dimensionality reduction. The EMOTIC dataset's fine-grained emotional categories, combined with contextual data, improved emotion detection in real-world settings, particularly when facial expressions alone were insufficient. LPQ further enhanced texture analysis in challenging environments with variable lighting. While CNNs demonstrated strong performance, challenges like real-time processing and generalization across diverse datasets remain. Future work should focus on integrating audio and physiological data and incorporating temporal information to detect dynamic emotional changes. These developments will help only in creating a better and generalized emotion detection system for the respective areas like healthcare, virtual reality, and interaction with the virtual world.