Emotion Detection System – Real-Time Facial Expression Recognition

Vision

This project aimed to build a robust, real-time emotion recognition system capable of detecting and classifying human facial expressions with high accuracy. By combining deep learning with real-time video processing, the system is positioned for applications in human-computer interaction, surveillance, and sentiment-driven customer service.

Approach

• Model Architecture: Developed a Convolutional Neural Network (CNN) trained to classify a range of facial expressions. The model achieved 87% accuracy on the test dataset.

• Real-Time Processing: Integrated OpenCV for live face detection and frame capture, allowing the model to process both static images and continuous video streams.

• Data Preprocessing: Standardized input data with techniques such as grayscale conversion, normalization, and face cropping to improve model consistency and reduce noise.

• Robustness Enhancements: Implemented preprocessing pipelines to account for variations in lighting, facial angles, and occlusions, improving real-world performance.

• User Interface: Created a lightweight interface to display predictions and confidence levels, providing clear real-time feedback to users.

Challenges

• Ensuring consistent face detection in real-time under varied environmental conditions (e.g., lighting, background clutter).

• Preventing performance degradation on low-resolution video feeds or lower-end hardware.

• Fine-tuning the CNN to balance speed and accuracy, particularly when deployed in live camera scenarios.

Conclusion

The emotion detection system effectively combines computer vision and deep learning to interpret human emotions in real time. With strong accuracy and adaptability, it demonstrates practical use across multiple industries. The project reinforced key machine learning principles, model deployment strategies, and the importance of optimizing for both performance and user experience.