Build a Hardware-based Face Recognition System for $150 with the Nvidia Jetson Nano and Python

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With the Nvidia Jetson Nano, you can construct robust stand-alone hardware systems capable of running GPU-accelerated deep learning models on a modest budget. It’s similar to a Raspberry Pi, yet significantly faster.

Introduction to the Project

Using face recognition and Python, you can effectively monitor anyone approaching your door. Let’s embark on a practical hardware project utilizing the Jetson Nano to create a simplified version of a doorbell camera. This system can track individuals arriving at your home’s front door, recognizing them instantly—even if their appearance has changed. If they have previously visited, it can log exactly when and how often.

Understanding the Nvidia Jetson Nano

For many years, Raspberry Pi has been the go-to choice for developers interested in building hardware devices. The Raspberry Pi is a $35 computer-on-a-board that runs Linux and fully supports Python, allowing users to create stand-alone computer vision systems with additional components, such as a $20 camera module.

While the Raspberry Pi is remarkable, it can struggle with deep learning applications due to its non-existent GPU and relatively slow CPU, leading to laggy performance. Recognizing this gap, Nvidia developed the Jetson Nano—a Raspberry Pi-like device featuring an embedded GPU, specifically designed for efficient deep learning model execution.

The Jetson Nano operates as a Linux computer on a single board, boasting a 128-core Nvidia GPU for deep learning acceleration. It also supports CUDA libraries, allowing existing Python-based deep learning frameworks to run on the Jetson Nano with minimal adjustments.

What You’ll Need

To kickstart this hardware project, gather the following components:

  1. Nvidia Jetson Nano board ($99 USD) – Ensure you purchase from a reputable source to avoid scams.
  2. MicroUSB power plug (~$10 USD) – Use a compatible power adapter that meets the Jetson Nano’s power requirements.
  3. Raspberry Pi Camera Module v2.x (~$30 USD) – Ensure it’s a v2.x version, as the earlier model isn’t supported by the Jetson Nano.
  4. Fast microSD card (at least 32GB, ~$10-$25 USD) – Consider a larger capacity card to avoid running out of storage.
  5. Additional hardware you might already have at home:
  • microSD card reader for software installation.
  • Wired USB keyboard and mouse for setup.
  • Monitor or TV with HDMI support for setup.
  • Ethernet cable (the Jetson Nano does not support Wi-Fi natively).

By gathering these components, you can complete the project for under $150.

Setting Up the Jetson Nano Software

Before assembling your hardware, download the required software image:

  1. Download the Jetson Nano Developer Kit SD Card Image from Nvidia.
  2. Use Etcher to write this image onto your microSD card (this process takes about 20 minutes).

Assembling the Hardware

  1. Insert the microSD card into the Jetson Nano’s SD card slot.
  2. Connect the Raspberry Pi v2.x camera with the appropriate ribbon cable.
  3. Plug in the keyboard, mouse, and monitor, followed by connecting the power supply to boot the device.

Initial Setup and Configuration

On first boot, follow the typical Ubuntu Linux user creation process. Pytho3 and OpenCV will already be installed. Next, ensure you install the necessary Python libraries for your project:

sudo apt-get update
sudo apt-get install python3-pip cmake libopenblas-dev liblapack-dev libjpeg-dev

Next, you may need to create a swapfile to aid compilation of necessary libraries due to the device’s RAM limitations.

Installing Additional Libraries

Install NumPy and Dlib, as they are essential for our face recognition application:

pip3 install numpy

Finally, install the face_recognition library, which simplifies the face detection process.

Implementing the Face Recognition System

Write a Python application to capture video from the camera, detect faces, and log visits. Your script will handle the main loop, calling various functions to capture frames, process faces through the recognition pipeline, and display results in real-time.

Testing and Optimizing the System

Thoroughly test your system under various conditions to ensure reliability.

By taking these steps, you’ll create a functioning hardware-based face recognition system powered by the Nvidia Jetson Nano.

Images Created:

  1. A sleek and modern hardware-based face recognition system built using Nvidia Jetson Nano:
    A sleek and modern hardware-based face recognition system built using Nvidia Jetson Nano. The setup includes the Jetson Nano board, a Raspberry Pi camera module, and a well-organized workspace with tools and components. Emphasize the technology aspect with a techy background.
  2. Workflow of a face recognition doorbell camera system:
    An illustrative diagram showing the workflow of a face recognition doorbell camera system. Include elements like 'Camera captures image', 'Face detection', 'Face recognition', 'Identify visitor', and 'Log visit'. Use a clean, informative style with arrows connecting each step.
  3. A close-up shot of the Nvidia Jetson Nano board on a workbench:
    A close-up shot of the Nvidia Jetson Nano board on a workbench, showcasing its GPU and ports. The background should be blurred to focus on the Jetson Nano, making it look tech-savvy and advanced.
  4. Face recognition technology in action:
    A visual representation of face recognition technology in action, showing a person standing in front of a doorbell camera. Include overlay graphics showing facial features being detected and processed by the system.

Feel free to ask if you have any more questions!

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