AI Attendance System

An intelligent attendance management system that uses AI-powered face recognition and phone camera integration to automate and simplify attendance tracking.

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Table of Contents

• About the Project
• Features
• Tech Stack
• Getting Started
• Installation
• Usage
• Project Structure
• Screenshots
• Contributors

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About the Project

The AI Attendance System is a smart, contactless attendance solution built for classrooms, offices, or any organization. It leverages real-time face recognition through a phone camera to automatically detect and mark attendance — eliminating the need for manual roll calls or ID cards.

Features

• Real-time Face Recognition — Detects and identifies faces instantly
• Phone Camera Support — Use your phone as a wireless camera
• Automatic Attendance Logging — Records attendance with timestamp
• Multi-face Detection — Recognizes multiple people simultaneously
• Secure & Contactless — No physical interaction required
• Attendance Records — Stores data for future reference

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🛠 Tech Stack

Technology	Purpose
Python	Core programming language
OpenCV	Video frame capture & face bounding box rendering
InsightFace (buffalo_sc)	AI face detection & 512-dim embedding generation
scikit-learn	Cosine similarity for face matching
Redis (RedisLabs Cloud)	Cloud database for face embeddings & attendance logs
NumPy	Embedding vector operations & mean calculation
Pandas	Attendance log processing & report generation
Streamlit	Web-based UI
Phone Camera (IP Webcam)	Remote camera streaming

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Getting Started

Prerequisites

Make sure you have the following installed:

• Python 3.8 or higher
• pip (Python package manager)
• A smartphone with IP Webcam app installed (for phone camera)

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Installation

1. Clone the repository

git clone https://github.com/jayraval30/AI-Attendance-System.git
cd AI-Attendance-System

2. Install required dependencies

pip install -r requirements.txt

3. Run the application

streamlit run Home.py

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Usage

Using Phone Camera

1. Install IP Webcam app on your Android phone
2. Start the server in the app.
3. Copy the IP address shown (e.g., http://192.168.x.x:8080)
4. Paste it in the app when prompted.

Taking Attendance

1. Launch the app with streamlit run Home.py
2. Register faces by uploading photos or using live camera.
3. Start attendance — the system will auto-detect and mark present students/employees.
4. View and export the attendance log

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Project Structure

AI-Attendance-System/
│
├── assets/                  # Images and static files
├── pages/
│   ├── 1_Real_Time_Prediction.py   # Live face recognition & attendance marking
│   ├── 2_Registration_form.py      # Student face registration
│   └── 3_Report.py                 # Attendance report & analytics
├── Home.py                  # Main application entry point
├── face_rec.py              # Core face recognition logic & Redis operations
├── phone_camera.py          # Phone camera stream handler
├── test_phone_cam.py        # Camera testing script
├── upload_logs.py           # Redis log upload utility
├── video.py                 # Video processing utility
├── requirements.txt         # Python dependencies
├── .gitignore               # Git ignore rules
└── README.md                # Project documentation

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Screenshots

Screenshots and demo coming soon...

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License

This project is licensed under the MIT License — feel free to use and modify it