A 3-layer CNN (32–64–128 filters,
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A 3-layer CNN (32–64–128 filters, dropout 0.5) for 7-class facial emotion classification on the CK+ dataset. Achieved 97% test accuracy and a 0.97 weighted F1, benchmarked against KNN, SVM, MLP, and MobileNetV2. Full pipeline: preprocessing, training, evaluation, and model comparison.
Stack: Python, TensorFlow, OpenCV, scikit-learn
Link: github.com/MohanadddAmrr/Emotion-Recognition-CNN
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A 3-layer CNN (32–64–128 filters, dropout 0.5) for 7-class facial emotion classification on the CK+ dataset. Achieved 97% test accuracy and a 0.97 weighted F...
