Attendance System Using Webcam

This project is an attendance system that utilizes a webcam to detect and recognize faces. It employs the following technologies:

• OpenCV: For image and video processing
• Face Recognition: For face detection and recognition
• Pickle: For storing and retrieving face data
• Streamlit: For creating a user-friendly web interface

Getting Started

1. Install Dependencies
   Make sure you have the following Python libraries installed:

• NumPy
• Pandas
• OpenCV
• Face Recognition
• Pickle
• Streamlit

2. Data Preparation
   Run the faces_data.py file to create or update the pickle file containing face data and names. If the pickle file already exists, it will append new face data and names to the existing file. The face detection is performed using the Haar Cascade classifier.

3. Take Attendance
   Run the take_attendance.py file to open the webcam and start the attendance process. Press the 'O' key to record the attendance of the recognized person. The system will provide an audio feedback saying "Attendance taken".

4. View Attendance Records
   To view the attendance records, run the app.py file using the following command:

streamlit run app.py

This will launch a Streamlit application in your default web browser, displaying the attendance records.

Project Structure

• faces_data.py: Script to create or update the pickle file with face data and names.
• take_attendance.py: Script to open the webcam, detect faces, and record attendance.
• app.py: Streamlit application to display attendance records.
• haarcascade_frontalface_default.xml: Haar Cascade classifier for face detection.
• data.pickle : Pickle file containing face data and names.

Usage

1. Run faces_data.py to create or update the face data pickle file.
2. Run take_attendance.py to start the attendance system.
3. Press 'O' to record the attendance of recognized faces.
4. Run streamlit run app.py to view the attendance records in a web interface.

Please note that this project requires a webcam to be connected to your system for face detection and attendance recording.