😄 Emotion Classifier using Deep Learning

This project uses a Convolutional Neural Network (CNN) to classify human facial emotions based on the FER2013 dataset. It detects emotions like Happy, Sad, Angry, Surprise, and more — directly from grayscale face images.

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📷 Sample Output

▶️ Emotion Detection Results

Here are some examples of predictions made by the trained model:

✅ Add your own screenshots in the screenshots/ folder for better visuals!

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📁 Dataset

• Source: FER2013 (Facial Expression Recognition)
• 48x48 grayscale images of faces
• Emotions:
  • Angry
  • Disgust
  • Fear
  • Happy
  • Sad
  • Surprise
  • Neutral

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🧠 Model Architecture

• Input: 48x48 grayscale image
• Layers:
  • Conv2D → ReLU → MaxPooling
  • Dropout for regularization
  • Dense → Softmax (multi-class classification)
• Optimizer: Adam
• Loss Function: Categorical Crossentropy

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📊 Training Results

Metric	Value
Accuracy	XX% (fill yours)
Loss	XX
Epochs	30
Batch Size	64

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📂 Files

File	Description
Emotion_Classifier.ipynb	Complete training notebook (Google Colab)
emotion_classifier_model.h5	Trained Keras model
screenshots/	Folder for output screenshots
README.md	Project overview and documentation

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🚀 Future Plans

• Deploy using Streamlit
• Integrate with OpenCV for real-time webcam emotion detection
• Improve accuracy with data augmentation and ResNet

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🧑‍💻 Author

Muhammad Rayan Shahid
Passionate AI & ML Developer | LinkedIn | GitHub

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