🎯 Face Recognition Attendance System (ML + DL)

📌 Project Overview

This project is a Smart Attendance System built using Machine Learning and Deep Learning techniques. It captures student faces using a webcam, extracts facial features, and recognizes individuals in real-time.

The system maps recognized faces with a CSV database and displays:

• 👤 Name
• 🆔 Roll Number
• 📈 Confidence Score

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🚀 Features

• 📷 Real-time face detection using Deep Learning (Caffe SSD)
• 🧠 Face recognition using OpenFace embeddings
• 🤖 Machine Learning classification using SVM
• 🗂️ Automatic dataset creation
• 📊 CSV-based student database
• ⚡ Fast and accurate recognition system
• 🖥️ Real-time UI with bounding boxes and labels

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🧠 Algorithms & Models Used

🔍 Face Detection

• Deep Learning-based SSD (Single Shot Detector)

• Caffe Model:

  • res10_300x300_ssd_iter_140000.caffemodel
  • deploy.prototxt

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🧬 Face Embedding (Feature Extraction)

• OpenFace Deep Neural Network

• Model:

  • openface_nn4.small2.v1.t7

• Converts face → 128-dimensional vector

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🤖 Face Recognition (Classification)

• Support Vector Machine (SVM)

  • Kernel: Linear
  • Probability estimation enabled

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🔤 Label Encoding

• Converts names → numerical labels using:

  • LabelEncoder

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🛠️ Technologies & Libraries Used

Core Libraries

• numpy
• opencv-python
• imutils
• scikit-learn

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Additional Libraries

• pickle (model saving/loading)
• csv (database handling)
• os (file management)
• time (camera delay handling)

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⚙️ Installation

1️⃣ Clone Repository

git clone git remote add origin https://github.com/selvan-01/face-recognition-attendance-system.git
cd face-recognition-attendance-system

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2️⃣ Install Dependencies

pip install -r requirements.txt

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▶️ How to Run

Step 1: Create Dataset

python src/dataset_creation.py

Step 2: Generate Face Embeddings

python src/preprocess_embeddings.py

Step 3: Train ML Model

python src/train_face_model.py

Step 4: Run Face Recognition

python src/recognize_with_database.py

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📊 Output

• Real-time face detection

• Displays:

  • 👤 Name
  • 🆔 Roll Number
  • 📈 Confidence %

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🔥 Future Enhancements

• ✅ Excel-based attendance system
• ⏰ Timestamp recording
• 🌐 Web dashboard
• 📱 Mobile integration
• ☁️ Cloud deployment

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🔗 Links

• 💼 LinkedIn
• 🌍 Portfolio
• 💻 GitHub

🙌 Author

S. Senthamil Selvan (Sen) 🎓 Computer Science Engineering Student 🚀 AI | ML | Data Science Enthusiast

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⭐ Support

If you like this project:

• ⭐ Star the repository

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💡 Conclusion

This project demonstrates how Machine Learning + Deep Learning can be used to automate real-world systems like attendance tracking with high accuracy and efficiency.