EMOTICON: Real-Time Video Feed Emotion Detection Enhanced by Particle Swarm Optimization and GRAD-CAM Analysis

In recent years, facial expression-based emotion recognition has attracted a lot of interest, especially for real-time applications in fields like healthcare, security, and human-computer interaction. Image processing plays a critical role in a wide range of applications, offering enhanced capabilities for data analysis and feature extraction across various domains. A significant aspect of this project is emotion detection, where image processing techniques are applied to recognize and classify human emotions from facial expressions. To enhance accuracy and interpretability, we introduce EMOTICON, a novel method for real-time emotion recognition from video feeds that combines Grad-CAM (Gradient-weighted Class Activation Mapping), Particle Swarm Optimization (PSO), and Convolutional Neural Networks (CNN). The objective is to enhance both the accuracy and interpretability of emotion recognition systems. To achieve this, we suggest comparing three distinct emotion detection configurations: (1) CNN-based emotion detection, (2) CNN and PSO for optimization, and (3) CNN integrated with Grad-CAM and PSO for enhanced performance and visual representation of the identified features. In order to improve the model's efficiency and capacity for generalization, PSO optimizes the model's parameters while CNN extracts feature maps from facial images during the training phase. By systematically exploring and applying these techniques, the project provides valuable insights into the multifaceted nature of image processing and its transformative potential in contemporary technological applications. Our tests demonstrate that CNN, PSO, and Grad-CAM collectively contribute to enhanced accuracy, which improves performance and provides interpretable insights about the model's decision-making process. The comparative analysis's findings show how effective it is to incorporate interpretability and optimization strategies into emotion detection systems.