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🎭 Facial Emotion Recognition (FER) Using Custom CNN

A high-accuracy Facial Emotion Recognition model built using a Custom Convolutional Neural Network. This project classifies human emotions from facial images into seven categories and is optimized for real-time applications.

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🚀 Features

• ✔️ Custom-designed CNN architecture
• ✔️ High accuracy on FER datasets
• ✔️ Real-time emotion prediction support
• ✔️ Lightweight & fast inference
• ✔️ Data augmentation for robust training
• ✔️ Easy to integrate into other applications

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🧠 Emotions Detected

The model predicts the following 7 emotions:

• Angry
• Disgust
• Fear
• Happy
• Sad
• Surprise
• Neutral

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🏗️ Model Architecture (Custom CNN)

The CNN consists of multiple convolutional blocks with increasing depth, followed by fully connected layers:

• Input: 48×48 grayscale facial images
• Conv2D layers + ReLU activation
• Batch Normalization
• MaxPooling for downsampling
• Dropout for regularization
• Dense layers with ReLU
• Final Softmax output layer

✔️ Designed for high accuracy ✔️ Stable and efficient training ✔️ Prevents overfitting via dropout + augmentation

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📊 Performance

The Custom CNN achieved excellent accuracy during training and validation.

👉 Replace the line below with your accuracy: 📌 Model Accuracy: 89% on FER validation dataset

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📁 Dataset

This model can be trained on datasets like:

• FER-2013 (Kaggle)
• Custom facial emotion datasets

Images are preprocessed by:

• Converting to grayscale
• Resizing to 48×48
• Normalization
• Augmentation for robustness

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🧪 Training Setup

• Framework: TensorFlow / Keras
• Loss Function: Categorical Cross-Entropy
• Optimizer: Adam
• Augmentation: Rotation, shift, zoom, horizontal flip
• Epochs: Until validation accuracy converges
• Batch Size: Tuned for GPU efficiency

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📦 Project Structure (example)

├── dataset/
├── models/
│   └── emotion_cnn.h5
├── src/
│   ├── train.py
│   ├── model.py
│   └── predict.py
├── README.md
└── requirements.txt

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📷 Sample Output

(Add your model predictions or screenshots here)

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💡 Applications

• Human–Computer Interaction
• Smart surveillance
• Robotics
• Mental health monitoring
• Real-time emotion detection systems

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🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.

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📜 License

This project is licensed under MIT License (or your choice).

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