Table of Contents
The Automated Management Systems (AMS) is an innovative IoT project focused on enhancing security and privacy in a controlled company environment. Addressing the challenge of granting controlled access without compromising sensitive information, AMS introduces a comprehensive solution.
AMS integrates facial recognition and remote access technologies to regulate entry, ensuring only authorized personnel enter the controlled environment. It employs sophisticated security monitoring to restrict access to specific devices, preventing unauthorized usage within the environment.
This system optimizes energy consumption by selectively powering on devices based on user permissions. When multiple individuals are present, AMS requires facial recognition or QR code authentication for device activation, ensuring access control.
Additionally, AMS implements secure login protocols for devices, requiring facial recognition and/or QR code authentication, thereby enhancing security measures within the controlled environment. By providing detailed access control and authentication mechanisms, AMS ensures heightened security and restricted access while optimizing resource utilization.
This Architecture drawing is for a prototype system to prove the concept would work. The devices are smart plugs and in a real life system the button and code to start a device should be connected to the device itself.
- User Presses one of the five Buttons on the breadboard
- Facial Recognition Script running on the Raspberry Pi executes the related code for the button pressed
- If the user is granted access to the device a HTTP Request is sent to the Domain through a cloudflare tunnel to the Home Assistant Pi on the same Network.
- Node-Red receives the HTTP Request, turns on the correct Device, sends updates to all related cloud solutions and then sends a HTTP Response.
- Tuya Setup: Instructions in
Documentation/TuyaSetupdirectory. - Raspberry Pi 4 - Home Assistant: Instructions in
Documentation/HomeAssistantdirectory. - Raspberry Pi 5 - Facial Recognition: Instructions in
FacialRecognitiondirectory. - Website: Instructions in
Websitedirectory.
The following resources were used in the devlopment of this project:
- Home Assistant Tuya Integration instructions
- Read QR Codes from Raspberry PI with Pyzbar and Python
- Node-RED Cookbook
- Real-time Data Storage with Firebase and Node-RED
- Working with JSON Data And JavaScript Objects in Node-Red
- node-red-http-endpoint-examples
- Node-Red HTTP Request Node for Beginners
- Using a push button with Raspberry Pi GPIO
- The Easiest Free Way To Do Home Assistant Remote Access!
The following sites/videos/repos were viewed while researching how to create this project but might not have been used within the project:
- Deepface
- Object recognition with Python
- Raspberry Pi Pinout Guide: How to use the Raspberry Pi GPIOs?
- Tuya Convert
- SuperHouse #37: Installing Tasmota using Tasmotizer
- SuperHouse 44: Installing Tasmota over-the-air with Tuya-Convert
- Displaying OpenCV Video Feed on HTML Windows using Python
- Upload Images / Files to Firebase Cloud Storage using Node JS
- IoT Application using Node Red & Google Firebase | Raspberry Pi
- How to Work With JSON in Node-RED
- Getting started with Google Coral’s TPU USB Accelerator
- My first steps with Raspberry Pi Camera Module 3
- Face Recognition With Raspberry Pi + OpenCV + Python
- Face Recognition Based Complete Attendance System with Database and Webpage using PC or Raspberry Pi
- Face Recognition Using Opencv & Python | UNKNOWN Face Recognition | KNOWLEDGE DOCTOR
- Raspberry Pi and Movidius NCS Face Recognition
- Install OpenCV 4 on Raspberry Pi 4 and Raspbian Buster
- Face Recognition with OpenCV and Google Coral USB Accelerator
- Real-Time Face Recognition: An End-to-End Project
- Build Your Own Face Recognition Tool With Python
Your Name - Kieron Garvey
Project Link: https://github.com/ki321g/AMS/