Student Attendance System

Overview

The Student Attendance System is a Python-based application designed to simplify the process of student attendance using face recognition technology. This project combines the power of machine learning with an interactive PyQt GUI to offer a user-friendly and efficient solution for managing attendance records.

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Features

1. Face Recognition System

• Built using HOG (Histogram of Oriented Gradients) and ResNet models from the face_recognition library.
• Achieves an accuracy of 90% in predicting and recognizing student faces.
• Matches recognized faces against a database to mark attendance.

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2. PyQt5 GUI with Multi-Page Navigation

The GUI provides an intuitive interface with the following key panels:

Login Page

• Secure login with username and password. (for 1st time usename: op password: op)
• Access to:
  • Data Management Panel
  • Student Management Panel

Data Management Panel

• Add new users (usename name and password)
• Add new student (name,batch,roll number,year,and 3 photos to save it in training folder)
• Update existing user and student information.
• Encode faces and store them in the database for recognition.

Student Management Panel

• Input fields for:
  • Subject Name
  • Date & Time
  • Year and Batch
• Fetches the list of students (names, roll numbers, and batch) based on the selected criteria.
• Starts the Face Recognition System to:
  • Recognize faces in real time.
  • Mark recognized students as Present.
  • Automatically mark unrecognized or absent students as Absent upon closing attendance.
• Save attendance records for future use.

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How It Works

Face Encoding Process (encode.py)

1. All images of students are stored in the training folder.
   • Each student's images are stored in a separate subfolder named after the student (e.g., training/John_Doe/).
2. The encode.py script:
   • Reads all images from the training folder.
   • Encodes each face using the face_recognition library.
   • Saves the encodings along with their respective names into an output.pkl file.
3. The encoded data in the output.pkl file is used during face recognition to match and identify students.

Attendance Process

1. In the Student Management Panel:
   • Select the subject, date, time, year, and batch.
   • Fetch the student data for the selected batch.
   • Press Start Attendance to open the face recognition system.
2. The system:
   • Recognizes faces in real time using the encodings from output.pkl.
   • Marks recognized students as Present.
   • Marks others as Absent when attendance is closed.
3. Save the attendance data.

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Installation

Prerequisites

• Python 3.7 or above
• Required Python libraries:
  • PyQt5
  • OpenCV
  • face_recognition
  • NumPy
  • Pandas

Steps

1. Clone this repository:

   git clone https://github.com/Priyansh7999/Student-Attendance-System.git
   

2. Install the required libraries:

   pip install pyqt5 opencv-python face-recognition numpy pandas
   

3. Run the application::

   python main.py
   

Usage

1. Login to the system.
2. Navigate to the appropriate panel:
   • Data Management Panel to manage user/student data and encode faces.
   • Student Management Panel to mark attendance. -Use the Face Recognition System to identify students and save attendance records.

Future Improvements

1. Enhance the face recognition model for higher accuracy.
2. Integrate real-time database synchronization for cloud-based attendance management.
3. Add analytics and reporting features for attendance trends.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for review.

License

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