Published Paper Details:

In contemporary educational and healthcare settings, the well-being of students and patients is critical for their success and recovery. A real-time system utilizing OpenCV for multi-modal emotion analysis is introduced, tailored specifically for these environments. This system merges facial expression recognition from images with audio emotion recognition. By employing machine learning and computer vision algorithms, such as OpenCV for facial recognition, a Convolutional Neural Network (CNN) for image-based emotion detection, and a combination of Support Vector Classifier (SVC) and CNN for audio emotion recognition, it provides a comprehensive approach to understanding emotional states. Privacy is a cornerstone of this system, upheld through the use of non-intrusive and privacy-conscious technologies. It continuously assesses the multiple emotional states of students and patients during their activities, offering insights into their well-being without breaching their privacy. The web interface displays real-time emotion analysis results, including facial recognition status, identified emotions, and dynamic emotion distribution charts. This tool is invaluable for monitoring stress and emotional well-being in educational and healthcare settings. Its open and adaptable codebase allows for tailoring and modifications to meet specific needs, demonstrating the potential of real-time emotion analysis in improving the well-being and outcomes for students and patients.