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https://github.com/kiran-kumar-k3/face-attendance-system-dl-iot
Face Recognition Based Attendance System Using Deep Learning and IoT
https://github.com/kiran-kumar-k3/face-attendance-system-dl-iot
Last synced: 4 days ago
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Face Recognition Based Attendance System Using Deep Learning and IoT
- Host: GitHub
- URL: https://github.com/kiran-kumar-k3/face-attendance-system-dl-iot
- Owner: KIRAN-KUMAR-K3
- Created: 2024-10-19T06:09:55.000Z (28 days ago)
- Default Branch: main
- Last Pushed: 2024-11-02T16:12:56.000Z (13 days ago)
- Last Synced: 2024-11-02T17:20:04.522Z (13 days ago)
- Language: Python
- Size: 141 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Face Recognition Based Attendance System Using Deep Learning and IoT
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Technologies Used](#technologies-used)
- [Installation](#installation)
- [Usage](#usage)
- [Directory Structure](#directory-structure)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This project implements a Face Recognition Based Attendance System using Deep Learning techniques integrated with IoT. The system automatically detects and recognizes faces of students in real-time, marking attendance accordingly. This project aims to streamline the attendance process in educational institutions and minimize the potential for proxy attendance.
## Features
- Real-time face recognition for attendance marking
- Support for multiple users with string-based IDs (e.g., "4AL22CS405")
- Efficient and accurate detection using deep learning algorithms
- IoT integration for remote monitoring and attendance tracking
- User-friendly interface for viewing attendance reports
## Technologies Used
- Python
- OpenCV
- TensorFlow / Keras (for deep learning)
- Flask (for web interface)
- Raspberry Pi / Arduino (for IoT integration)
- Haar Cascade Classifier (for face detection)
## Installation
To set up the project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/KIRAN-KUMAR-K3/face-attendance-system-dl-iot.git
cd face-attendance-system-dl-iot
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Download the Haar Cascade file: Ensure the `haarcascade_frontalface_default.xml` file is in the project directory.
## Usage
1. Run the dataset creator to gather training data:
```bash
python datasetCreator.py
```
2. Train the model using the collected data:
```bash
python trainner.py
```
3. Start the application:
```bash
python app.py
```
4. Access the web interface: Open your web browser and navigate to [http://localhost:5000](http://localhost:5000) (or your device's IP address if using IoT).
5. Mark attendance: The system will automatically detect and recognize faces as they come into the camera's view.
## Directory Structure
```
face-attendance-system-dl-iot/
├── dataSet/ # Directory for storing training images
├── datasetCreator.py # Script to collect training data
├── haarcascade_frontalface_default.xml # Pre-trained face detection model
├── trainner.py # Script to train the model
├── app.py # Main application script
└── requirements.txt # Python dependencies
```
## Contributing
Contributions are welcome! If you have suggestions for improvements or want to report issues, please create an issue or submit a pull request.
## License
This project is licensed under the MIT License. See the LICENSE file for details.