Real-time facial emotion detection using optimized CNN architecture achieving ~82% accuracy. Built with TensorFlow/Keras for production deployment with grayscale optimization and data augmentation techniques. Outperforms transfer learning models while maintaining edge-compatible efficiency.
python data-science machine-learning computer-vision deep-learning tensorflow keras cnn neural-networks image-classification production-ready affective-computing emotion-recognition real-time-detection facial-emotion-detection grayscale-optimization mit-professional-education-adsp
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Updated
May 24, 2025 - HTML