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Emotion Recognition

Facial emotion classification on FER2013 achieving ~66% accuracy on a 3-class subset

What

An image classification model trained on the FER2013 dataset (48×48 px grayscale face images) to detect emotions. The implementation focuses on three classes: happy, neutral, and surprise. Achieved ~66% accuracy on the test set.

Why

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Approach

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Results

~66% accuracy on the 3-class test set.

Stack

| Layer | Choice | |---|---| | Language | Python | | Environment | Google Colab | | Dataset | FER2013 (Kaggle) | | Notebooks | Jupyter |