😊 Emotion Recognition System

The Emotion Recognition System is a computer vision project that detects human faces and classifies emotions in real-time using deep learning. It combines facial recognition and emotion detection to interpret expressions such as happiness, sadness, anger, surprise, and more — making it suitable for applications in mental health, customer analysis, and smart systems.

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🚀 Key Features

• 👤 Facial Recognition: Identifies individuals in live video feeds or images using pre-trained models such as OpenCV or deep learning frameworks (TensorFlow / Keras).
• 😄 Emotion Detection: Classifies emotions (e.g., happy, sad, angry, neutral) using models trained on datasets like FER2013.
• ⚡ Real-Time Processing: Processes video frames efficiently for instant recognition and emotion classification.
• 🖥️ Interactive Interface: Displays detected faces alongside their predicted emotions, with the potential to log results for further analysis.

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🧠 Why It’s Impressive

• Technical Depth: Demonstrates expertise in computer vision, real-time data processing, and deep learning.
• Practical Relevance: Can be adapted for use in security systems, customer sentiment tracking, or mental health monitoring.
• Cross-Disciplinary Application: Integrates AI, software engineering, and data science to solve real-world challenges.

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🧩 Tech Stack

• Language: Python
• Frameworks & Libraries: TensorFlow, Keras, OpenCV, NumPy, Matplotlib
• Dataset: FER2013 (not included due to size — see instructions below)
• Model Format: .h5 (trained model) and .json (label map)

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⚙️ How to Run the Project

1️⃣ Prepare Dataset

Download the FER2013 dataset from Kaggle or another source.
Organize it as follows:

Emotion rec/
├── fer2013/
│ ├── train/
│ │ ├── happy/
│ │ ├── sad/
│ │ ├── angry/
│ │ └── ...
│ └── test/
│ ├── happy/
│ ├── sad/
│ ├── angry/
│ └── ...

⚠️ Note: The FER2013 dataset is not included in this repository due to size limits.

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2️⃣ Set Up Environment

# Create a virtual environment
python3 -m venv venv
source venv/bin/activate          # macOS / Linux
venv\Scripts\activate             # Windows

# Install dependencies
pip install -r requirements.txt

3️⃣ Train the Model

python train_model.py

This will generate two files in your project directory:

emotion_model.h5 — the trained emotion detection model

labels.json — the mapping of class labels

You can tweak training settings such as batch size and epochs in train_model.py.

💡 Training can be slow on CPU — GPU acceleration with TensorFlow is recommended.

4️⃣ Run Real-Time Detection

python detect_emotion.py

A webcam window will open showing live detections. Press q to quit the program.