Facial Expression Recognition with CNN 🎭

A deep learning project focused on detecting facial expressions and recognizing emotions using a Convolutional Neural Network (CNN). This project leverages a labeled dataset of facial expressions and aims to achieve high accuracy in emotion detection by employing advanced deep learning techniques.

🌟 Project Overview

Facial expression recognition plays a pivotal role in applications such as:

Human-Computer Interaction (HCI): Enhancing user experience by recognizing emotions.
Surveillance Systems: Detecting suspicious behavior.
Healthcare: Monitoring mental health and emotions.
Marketing: Analyzing customer reactions.
This project uses a CNN-based approach to classify facial expressions into distinct emotion categories, such as happy, sad, angry, surprised, etc.

🛠️ Features

Emotion Detection: Classifies images into predefined categories of facial expressions.
CNN Architecture: Uses convolutional layers for feature extraction and fully connected layers for classification.
Performance Metrics: Evaluates model performance with accuracy, precision, recall, and F1 score.
Visualization: Generates graphs for accuracy vs. epoch and loss vs. epoch to monitor training performance.
Custom Dataset Support: Easily adaptable for different datasets.

📂 Directory Structure

project/ ├── data/ # Dataset directory
├── src/ # Source code
│ ├── train.py # Training script
│ ├── evaluate.py # Evaluation script with graph generation
│ └── model.py # CNN model definition
├── results/ # Saved models and graphs
├── README.md # Project overview
├── requirements.txt # Dependencies
└── LICENSE # License information

🔧 Setup Instructions

Prerequisites

Ensure you have Python installed along with the following libraries:
TensorFlow / Keras
NumPy
Matplotlib
OpenCV (optional for image preprocessing)

Steps to Set Up

Clone the repository:
git clone https://github.com/YourUsername/FacialExpressionRecognition.git
cd FacialExpressionRecognition

Install dependencies:

pip install -r requirements.txt
Download or prepare your dataset and place it in the data/ directory.

Run the training script:

python src/train.py

Evaluate the model and generate performance graphs:

python src/evaluate.py

🧠 Model Architecture

The CNN model includes:

Convolutional Layers: Extract spatial features from input images.
Pooling Layers: Reduce spatial dimensions for computational efficiency.
Dropout Layers: Prevent overfitting.
Fully Connected Layers: Perform classification based on extracted features.

📊 Performance Metrics

Accuracy vs. Epoch graph
Loss vs. Epoch graph
Confusion Matrix for detailed evaluation
Classification Report with precision, recall, and F1 scores

📜 Dataset

This project uses Kaggle's Facial Expression Recognition dataset but can be adapted to other datasets. The dataset contains labeled images for emotions like Happy, Sad, Neutral, etc.

🚀 Future Enhancements

Increase Model Accuracy:
Experiment with advanced architectures (e.g., ResNet, EfficientNet).
Perform hyperparameter tuning.
Real-Time Emotion Detection:
Integrate the model with OpenCV for real-time video feed analysis.
Multi-Modal Emotion Detection:
Combine facial expressions with audio analysis for robust emotion detection.

🙌 Contributions

Contributions are welcome! If you'd like to enhance the project, please fork the repository and submit a pull request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

👤 Author

Rohith Macharla

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GitHub