🎓 BioTrack: Face Recognition Attendance System

BioTrack is a robust, GUI-driven desktop application built with Python for automating the student attendance process. It utilizes real-time face recognition (LBPH) via OpenCV to efficiently record attendance, eliminating the need for traditional manual tracking.

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✨ Features

• GUI-driven Interface: An intuitive user interface built with Tkinter for effortless navigation.
• Student Registration: Allows new users to register by capturing multiple face samples for training.
• Model Training: Trains the face recognition model (LBPHFaceRecognizer) on the captured image data.
• Automatic Attendance: Marks attendance for recognized students in real-time from a live camera feed.
• Data Management: Attendance records are stored in subject-wise CSV files (e.g., Attendance/Subject/).
• Attendance Reports: Calculates and displays the final attendance percentage for students across a chosen subject.
• Manual Override: Includes a feature to manually enter or correct attendance records (via takemanually.py).
• Audio Feedback: Uses pyttsx3 to provide voice notifications for successful operations or errors.

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🛠️ Technologies & Dependencies

Technology	Role
Python 3.x	Core language.
OpenCV (cv2)	Face detection (Haar Cascades) and recognition (LBPH).
Tkinter	Building the Graphical User Interface (GUI).
Pandas & NumPy	Data manipulation and numerical operations for attendance files and image arrays.
pyttsx3	Text-to-Speech functionality.

Installation

1. Clone the repository:

   git clone [https://github.com/YourUsername/BioTrack-Face-Recognition-Attendance.git](https://github.com/YourUsername/BioTrack-Face-Recognition-Attendance.git)
   cd BioTrack-Face-Recognition-Attendance
   

2. Install dependencies:

   pip install opencv-python numpy pandas pillow pyttsx3
   

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🚀 Setup and Usage Guide

Initial Setup

You must create the following essential folders in the project's root directory. These folders are required for the system to store student data, training images, and attendance records:

• StudentDetails
• TrainingImage
• TrainingImageLabel
• Attendance

Ensure the Haar Cascade XML files (haarcascade_frontalface_default.xml and haarcascade_frontalface_alt.xml) are also in the root directory.

Running the Application

Start the main application using the primary Python file:

python attendance.py

➡️ Core Workflow

1. Register: Use the "Register new student" option to enter the student's Enrollment Number and Name, and capture face samples (typically 50+ images).

2. Train: Click "Train Image" to train the facial recognition model using the new images. This step is mandatory after any new registration.

3. Take Attendance: Click "Take Attendance", enter the subject name, and the system will automatically start recognizing faces and marking attendance for the day.

4. View Report: Use "View Attendance" to aggregate all daily records and display the final attendance percentage for a subject.

📁 Project Files Overview

File Name	Function
attendance.py	The main file, containing the primary BioTrack GUI and application flow.
takeImage.py	Handles face sample capture for new student enrollment.
trainImage.py	Executes the LBPH algorithm to train the face recognition model.
automaticAttedance.py	Core module for real-time face detection, recognition, and writing daily attendance to CSV.
show_attendance.py	Reads and processes all daily CSV files to generate the final attendance report.
takemanually.py	Module for manually correcting or entering attendance records.
haarcascade_*.xml	Pre-trained XML files used by OpenCV for Haar Cascade face detection.

📸 Project Snapshots:

• Simple UI:

• Register New Student:

• Take Attendance:

• View Attendance:

Support the Project: Your support is highly appreciated! Please consider starring ⭐ this repository.