This project develops a deep learning model using CNNs and Residual Blocks to predict facial key-points. The application spans from emotion recognition in AI to driver monitoring systems.
- Model: Utilizes CNN architecture with Residual Blocks for effective spatial information capture.
- Dataset: Comprehensive dataset of facial images annotated with key-point coordinates.
- Training: Includes data augmentation and regularization techniques.
- Emotional AI: Detects human emotions and cognitive states.
- Driver Monitoring: Ensures driver safety and attentiveness.
- AFFECTIVA Auto: Explore emotional intelligence applications for automotive industry.