Face Recognition Attendance System

A real-time face recognition-based attendance system using Python, Streamlit, Redis, and InsightFace. This system detects faces, matches them with stored embeddings, and logs attendance in Redis.

🚀 Features

• Face Registration: Users can register their faces and roles (e.g., Student, Teacher).
• Real-time Face Recognition: Detects faces and matches them with stored embeddings.
• Attendance Logging: Logs check-in and check-out times.
• Reporting Dashboard: View attendance logs and generate reports.

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🛠 Project Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/face-recognition-attendance-system.git
cd face-recognition-attendance-system

2️⃣ Setting up Virtual Environment and Installing Dependencies

For consistent and reproducible results, it's recommended to use a virtual environment.

Mac/Linux

1. Open the terminal.
2. Navigate to the project directory:

   cd /path/to/project

3. Create a virtual environment:

   python3 -m venv venv

4. Activate the virtual environment:

   source venv/bin/activate

5. Install required dependencies:

   pip install -r requirements.txt
   

Windows

1. Open the Command Prompt or PowerShell.
2. Navigate to the project directory:

   cd \path\to\project

3. Create a virtual environment:

   python -m venv venv

4. Activate the virtual environment:

   venv\Scripts\activate

5. Install required dependencies:

   pip install -r requirements.txt
   
   

3️⃣ Configure Environment Variables

• Create a .env file using the example template:

cp .env.example .env

• Open the .env file and set your Redis credentials:

REDIS_HOST=your-redis-host
REDIS_PORT=your-redis-port
REDIS_PASSWORD=your-redis-password

4️⃣ Run the Application

streamlit run Home.py

This will start the Streamlit application. Access it at:

http://localhost:8501

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📂 Project Structure

📦 face-recognition-attendance-system
 ┣ 📂 insightface_model/       # Pretrained InsightFace models
 ┣ 📂 pages/                   # Streamlit pages (Real-time Prediction, Reports)
 ┣ 📜 face_rec.py              # Core face recognition logic
 ┣ 📜 Home.py                  # Main Streamlit dashboard
 ┣ 📜 Report.py                # Attendance reporting module
 ┣ 📜 requirements.txt         # Dependencies
 ┣ 📜 .env                     # Redis credentials (not in Git)
 ┣ 📜 .env.example             # Example env file (in Git)
 ┗ 📜 README.md                # Project documentation

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📌 How the System Works

1️⃣ Face Registration

• Users register their face using the camera.
• The system extracts facial embeddings using InsightFace.
• The extracted embeddings are stored in Redis under the key academy:register.

2️⃣ Real-Time Face Recognition

• The camera captures faces and extracts facial embeddings.
• The system compares these embeddings with stored embeddings in Redis.
• If a match is found, attendance is logged in Redis under attendance:logs.

3️⃣ Attendance Logging

• When a person is detected, an entry is logged as:

  Name@Role@Timestamp
  

• Redis stores this in a list under attendance:logs.

4️⃣ Attendance Reporting

• Users can view attendance logs in the Reports tab.
• The system generates a detailed attendance report, including:
  • Check-in time
  • Check-out time
  • Duration spent

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📊 Dashboard Overview

🖥 Home Page

• Start real-time face recognition.
• View detected faces and their roles.

📋 Reports Page

• View registered users.
• Refresh and check attendance logs.
• Generate attendance reports.

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🔗 Future Enhancements

• Add a database backup mechanism.
• Integrate with cloud storage for model updates.
• Improve face recognition accuracy with better embeddings.

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🙌 Contributors

• Chaitanya Malani (Lead Developer)

For any issues, please raise a GitHub issue or contact me.