Project (F04) InfoVideo: ESP: Multi-Model Emotion Recognition - DL and NLP For Enhanced Interaction

Emotion Sense Pro is a real-time facial emotion recognition system developed using deep learning and computer vision techniques. The system aims to accurately detect human emotions from live video streams by integrating a Haar Cascade-based face detection with a lightweight Convolutional Neural Network (mini-XCEPTION). The model processes video frames that are captured
through a webcam, extracts facial regions, applies preprocessing operations such as grayscale conversion and resizing, and classifies emotions including happy, sad, angry, neutral, surprised, and fearful. The project addresses the limitations of traditional emotion recognition systems, such as low accuracy, poor performance in dynamic environments, and restricted emotion categories. Emotion Sense Pro enhances recognition accuracy using the FER2013 dataset and ensures real-time performance suitable for practical applications. The system is implemented in Python with TensorFlow, Keras, and OpenCV, enabling efficient integration of machine learning and computer vision. Extensive testing including unit, integration, system, and performance tests demonstrates the model’s robustness, reliability, and responsiveness in real-time scenarios. The results show consistent performance with minimal latency and accurate detection under standard lighting conditions. Emotion Sense Pro has wide applicability in domains such as mental health monitoring, human–computer interaction, education technology, robotics, and intelligent surveillance. The project demonstrates the potential of deep learning in building emotion-aware systems and provides a solid foundation for future enhancements such as multimodal emotion analysis, advanced deep learning architectures, and mobile deployment.

Видео Project (F04) InfoVideo: ESP: Multi-Model Emotion Recognition - DL and NLP For Enhanced Interaction канала Dr. Sivaram Ponnusamy