✨ Attendance Recognition System ✨

Welcome to the Attendance Recognition System repository! This project is designed to streamline attendance tracking using facial recognition technology. Below is an overview of the project, its structure, and how to get started.

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🌐 Project Features

• 👨‍🎓 Student Dataset Creation: Easily create datasets for students using the data_creation.py script.
• 🕵️‍♂️ Facial Recognition: Detect and recognize student faces using advanced pre-trained models.
• 📃 Attendance Logging: Automatically log attendance into a CSV file (attendance_log.csv).
• 📊 Data Preprocessing: Preprocess and embed dataset images for improved model performance.
• 🏆 Model Training: Train an SVC model for robust facial recognition.
• 🔄 Dashboard Visualization: View attendance logs in an intuitive GUI dashboard.

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🔠 Repository Structure

Here’s a breakdown of the repository contents:

📂 Folders

• datasets/: Contains datasets of student images. These datasets are created using the data_creation.py script.
• model/: Stores pre-trained models:
  • deploy.prototxt.txt
  • res10_300x300_ssd_iter_140000.caffemodel
• output/: Stores output embedding files:
  • embeddings.pickle
  • le.pickle
  • recognizer.pickle

🗞️ Key Files

• Attendance-recognition.py: 🔍 The main script for recognizing student faces and logging attendance into attendance_log.csv.
• pre-processing.py: 🔄 Preprocesses dataset images and generates embeddings for model training.
• model-training.py: 🏋️‍♂️ Trains the SVC model for facial recognition.
• recognition.py: 🕵️ A standalone face detection script using data from student.csv.
• dashboard.py: 📊 Displays attendance logs in a graphical user interface (GUI) using data from attendance_log.csv.
• data_creation.py: 🖌 Script to create datasets for students.
• Other Files:
  • 🎮 openface.nn4.small2.v1.t7 and haarcascade_frontalface_default.xml: Pre-trained face detection models.
  • 📄 student.csv: Contains student data.
  • 📃 attendance_log.csv: Logs attendance records.
  • 🎨 block-diagram.png: A visual representation of the project workflow.

🚀 Getting Started

⚙️ Prerequisites

Ensure you have the following installed:

• 💻 Python 3.8
• 📖 Required Python libraries:
  • OpenCV
  • NumPy
  • Scikit-learn
  • Pickle

🔧 Setup

1. Clone the repository:

   git clone https://github.com/your-repo/FaceRec-Attendance.git
   cd attendance-recognition

🚪 Running the Project

1. 📸 Create Student Datasets:

   python data_creation.py

2. ⚙️ Preprocess Images:

   python pre-processing.py

3. 🏆 Train the Model:

   python model-training.py

4. 🕵️‍♂️ Recognize Faces & Log Attendance:

   python Attendance-recognition.py

5. 🔧 View Attendance Logs in GUI:

   python dashboard.py

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🔧 How It Works

1. 📸 Dataset Creation: Use data_creation.py to capture images of students and store them in the datasets/ folder.
2. 🔄 Preprocessing: The pre-processing.py script processes images and creates embeddings for better recognition.
3. 🏋️‍♂️ Model Training: Train the SVC model using model-training.py with the processed embeddings.
4. 🕵️ Face Recognition: The Attendance-recognition.py script identifies students from student.csv and logs their attendance.
5. 📊 Dashboard: Visualize attendance data in an interactive GUI via dashboard.py.

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📊 Example Output

• Attendance logs are stored in attendance_log.csv.
• Example:

  Student Name,Date,Time
  John Doe,2024-12-20,10:30 AM
  

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🔧 License

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