https://github.com/aneeshpatne/facerecognitionattendencesystem

Automated Face Recognition Attendance System using ML classifiers for real-time tracking through webcam with CSV logging capabilities.
https://github.com/aneeshpatne/facerecognitionattendencesystem

attendance facerecognition haar-cascade-classifier ml naive-bayes-classifier

Last synced: 18 days ago
JSON representation

Automated Face Recognition Attendance System using ML classifiers for real-time tracking through webcam with CSV logging capabilities.

• Host: GitHub
• URL: https://github.com/aneeshpatne/facerecognitionattendencesystem
• Owner: aneeshpatne
• Created: 2022-10-11T10:41:22.000Z (over 2 years ago)
• Default Branch: master
• Last Pushed: 2024-11-22T10:49:47.000Z (2 months ago)
• Last Synced: 2024-11-22T11:29:01.391Z (2 months ago)
• Topics: attendance, facerecognition, haar-cascade-classifier, ml, naive-bayes-classifier
• Language: Python
• Homepage:
• Size: 70.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Face Recognition Attendance System

A real-time attendance system using facial recognition with KNN, SVM, Random Forest, and Naive Bayes classifiers.

## Features

- Face detection and recognition using webcam

- Multiple classification models:

    - K-Nearest Neighbors (KNN)

    - Support Vector Machine (SVM)

    - Random Forest

    - Naive Bayes

- Attendance logging with entry/exit tracking

- CSV-based attendance records

- Training interface for new faces

## Project Structure

```

.

├── FaceRecogKnn.py      # Main recognition script

├── Train_main.py        # Model training script

├── attendance.csv       # Attendance log file

├── classifier/          # Trained model storage

└── train_image/         # Training images directory

```

## Requirements

- Python 3.x

- OpenCV (cv2)

- face_recognition

- scikit-learn

- pandas

- numpy

- matplotlib

## Usage

1. Add training images:

     - Create a folder with the person's name under `train_image`

     - Add multiple face images of the person in their folder

2. Train the model:

     ```python

     python Train_main.py

     ```

3. Run attendance system:

     ```python

     python FaceRecogKnn.py

     ```

## How It Works

- `train()` function trains multiple classifiers using face encodings

- `predict()` detects faces and predicts identity

- `record_attendance()` logs attendance with timestamp

- System supports both entry and exit time tracking

- Real-time face detection with OpenCV webcam integration

## Attendance Format

The `attendance.csv` file tracks:

- Name

- Date

- Time

- Entry/Exit status

## Performance

The system evaluates accuracy across multiple classifiers:

- KNN

- SVM

- Random Forest

- Naive Bayes

Model performance metrics are displayed during training.