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Hey there! 👋 I'm Itai Mizlish , a passionate fullstack web developer with a knack for crafting captivating digital experiences using the magical world of JS frameworks and magical UI libraries. 🚀 When I'm not immersed in lines of code, you'll find me exploring the diverse landscapes of Israel, contributing to open source projects, and getting lost in the time-traveling adventures of Doctor Who! 🌌🌟 You can read more about me
2023 Itai Mizlish
RPI5-Door Lock with Face Recognition 🏠🔐
Published on
In this post, we'll take you through our journey of building a secure door lock system powered by the Raspberry Pi 5. Leveraging embedded deep learning, an AI camera module, and a full-stack application, we’ve created a robust, real-time face recognition system that controls door access.
"Security is not a product, but a process."
— Bruce Schneier
Introduction 🤖
The goal of this project is to create a secure door lock that only grants access when an authorized face is detected. Built on the Raspberry Pi 5 and powered by advanced deep learning techniques, our system not only secures your entryway but also delivers an engaging, real-time experience. Imagine this: the moment the camera sees your face, the door unlocks. That’s the big “wow” moment!
Watch the Demo:
Notice how the door unlocks instantly upon face recognition.
Tip: Double-check all connections and GPIO assignments before powering up. 🔌
Embedded Deep Learning & Face Recognition 🧠
At the heart of our system is a highly optimized face recognition pipeline. We use OpenCV for detection and frameworks like TensorFlow Lite (or PyTorch) for recognition inference.
Face Recognition Code Snippet
Delivery Classification with YOLOv8 🚚
For unknown faces, the system leverages a YOLOv8 model to detect delivery-related objects (boxes, helmets, motorcycles, etc.). If such items are found, the visitor is flagged as a potential delivery person.
Key Points:
Roboflow Integration:
Manage and annotate your dataset easily on Roboflow. Upload frames (20 seconds before/after unknown face events) for ongoing model retraining.
IMX500 Export:
For edge inference directly on the camera, export your YOLOv8 model to an IMX500-compatible format—reducing load on the Pi.
Note: If your camera isn’t IMX500-compatible, you can run YOLOv8 on the Pi’s CPU or an attached accelerator (like Coral TPU).
This project seamlessly fuses embedded systems, deep learning, and full-stack development to deliver a smart, secure door lock. By combining the Raspberry Pi 5, an AI-optimized camera, and cutting-edge inference techniques like YOLOv8 and IMX500 exports, we bring robust face recognition and delivery classification right to the edge. The integrated Node.js server, MongoDB database, and React front-end ensure you can monitor and control your door lock in real time.
Ready to build your own? Fork our repo, grab a Pi, and start tinkering. The future of edge AI is here—watch the demo and see it for yourself!
Happy hacking and stay secure!
For questions or feedback, feel free to reach out or open an issue on our GitHub repository.
This revised structure emphasizes the immediate impact of the demo video, ensuring that visitors instantly grasp the system’s real-world functionality.
Hey there! 👋 I'm Itai Mizlish, a passionate fullstack web developer with a knack for crafting captivating digital experiences using the magical world of JS frameworks and magical UI libraries. 🚀 When I'm not immersed in lines of code, you'll find me exploring the diverse landscapes of Israel, contributing to open source projects, and getting lost in the time-traveling adventures of Doctor Who! 🌌🌟 You can read more about me here
