Deep Learning Model for Detecting Emotions Through Pipeline

Human emotions, a pervasive psychological phenomenon, significantly influence individuals' daily experiences and mental health. In today's society, emotional well-being has become a major focus of concern. Facial expressions convey a diverse array of emotions, including natural, joy, sorrow, revulsion, terror, rage, astonishment, and disdain. This research introduces an innovative real-time emotion detection system that analyzes facial images using a multi-kernel approach within a deep learning framework. A notable contribution is the implementation of a Support Vector Machine (SVM) classifier with multiple kernel functions, emphasizing the improved accuracy and decreased processing time achieved by the Polynomial Kernel. This model demonstrates substantial enhancements in precision and efficiency compared to existing single-kernel methods, making it ideal for real-time applications. The study explores the effectiveness of emotional indicators derived from social media platforms, such as chatbots, messages, photographs, and facial expressions. The paper proposes a deep learning-based workflow for emotion recognition through facial expressions, utilizing a multi-kernel approach. The model incorporates Linear, Radial Basis Function (RBF), and Polynomial kernels, with the Polynomial Kernel exhibiting superior performance, attaining the highest cross-validation accuracy of 77.97% on the FER-2013 dataset. This research provides a thorough assessment of the model's accuracy and computational efficiency, demonstrating its competitive edge over current emotion recognition techniques.