https://github.com/pradeep-r04/attendiq
AttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins. The system captures facial data during a quick registration process .
https://github.com/pradeep-r04/attendiq
csv cv2 kneighborsclassifier numpy os pandas pickle python scikit-learn streamlit time
Last synced: about 1 month ago
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AttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins. The system captures facial data during a quick registration process .
- Host: GitHub
- URL: https://github.com/pradeep-r04/attendiq
- Owner: pradeep-r04
- Created: 2025-04-11T06:39:07.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-04-11T07:06:07.000Z (10 months ago)
- Last Synced: 2025-08-22T03:49:05.056Z (6 months ago)
- Topics: csv, cv2, kneighborsclassifier, numpy, os, pandas, pickle, python, scikit-learn, streamlit, time
- Language: Python
- Homepage:
- Size: 3.79 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎯 AttendIQ – Face Recognition Attendance System
AttendIQ is a Face Recognition Attendance System designed to automate and streamline the attendance process with precision and ease. By leveraging real-time face detection and recognition technology, AttendIQ eliminates the need for manual roll calls or ID-based check-ins. The system captures facial data during a quick registration process .
---
## 🚀 Features
- 🔍 Real-time Face Detection & Recognition
- 🧑💼 User Registration with ID & Name
- 📸 Captures only 5 face samples per user
- ✅ Attendance marked only on pressing the `o` key
- 🗂 Attendance stored in timestamped CSV files
- 🗣 Voice feedback for successful attendance
- 📊 Streamlit dashboard to view attendance data
- 📁 Modular structure with separate files for training, recognition, and interface
---
## 🧰 Technologies Used
- Python 3.x
- OpenCV
- NumPy
- Scikit-learn (KNN)
- Streamlit
- win32com (for text-to-speech on Windows)
- CSV, Pickle (for data storage)
---
## 📁 Project Structure
face_recog/ ├── data/ │ ├── haarcascade_frontalface_default.xml │ ├── names.pkl │ └── faces_data.pkl ├── Attendance/ │ └── Attendance_dd-mm-yyyy.csv ├── ├── main.py # User registration and sample capture ├── test.py # Face recognition & attendance logging ├── app.py # Streamlit interface └── README.md
---
## 🧑🎓 How It Works
1. **Register User (main.py)**
- Input user ID and Name
- System captures 5 face samples
- Saves data into `faces_data.pkl` and `names.pkl`
2. **Recognize & Mark Attendance (test.py)**
- Launches webcam feed
- Detects and recognizes registered faces
- Press `o` key to log attendance into a dated CSV file
- Press `q` to exit
3. **Streamlit Dashboard (app.py)**
- Run the UI with `streamlit run app.py`
- Register users, capture faces, and view attendance data in a user-friendly interface
---
## ▶️ Getting Started
## 📌 Notes
Ensure your webcam is working properly.
Press o to mark attendance after face is recognized.
Each user is registered with exactly 5 face samples.
Attendance records are saved in the Attendance/ folder, labeled by date.
## 🙌 Acknowledgements
OpenCV – for real-time face detection
scikit-learn – for implementing KNN classification
Streamlit – for making the interface interactive
Microsoft Speech API – for text-to-speech feature
## 📜 License
This project is for educational and personal use only.
## 💡 Future Enhancements
Database integration (e.g., SQLite or Firebase)
Email/SMS notification support
Admin login for secured access
Attendance analytics dashboard
## Connect with me:
| Name | Email | LinkedIn | GitHub | Instagram |
|---------|--------------------|-----------------------------------------------|-----------------------------|-------------------------------|
| Pradeep | [Email](pradeep.singh04r@gmail.com) | [LinkedIn](https://linkedin.com/in/pradeep-singh4) | [GitHub](https://github.com/pradeep-r04) | [Instagram](https://instagram.com/whypradeeep) |