Build a Face Recognition Machine With Jetson Nano

Get ready to work with a powerful combination of technologies! This project involves setting up your Jetson Nano, installing the Jetpack SDK, and mastering image processing with OpenCV. For the machine learning component, you'll be using TensorFlow.

NVIDIA Jetson Nano Developer Kit (945-13450-0000-100):

https://www.amazon.com/NVIDIA-Jetson-Nano-Developer-945-13450-0000-100/dp/B084DSDDLT

Compatible USB Camera: Search for "USB webcam Jetson Nano" on Amazon. Consider a high-resolution option like: Logitech C920x HD Pro Webcam: Here!

Micro SD Card: (Choose a large capacity, fast card)

Samsung EVO Select 256GB microSDXC: Here!

Monitor, Keyboard, Mouse: (You may already have these)

Optional, but Recommended

Ethernet Cable: For better internet connectivity during setup.

Amazon Basics Cat 6 Ethernet Patch Internet Cable - 15 Feet

Heat Sink and/or Fan: To improve performance under load.

Search for "Jetson Nano heatsink" or "Jetson Nano Cooling Fan" .

Project Case: To protect your Jetson Nano setup.

Search for "Jetson Nano Case" on Amazon.

Purchase: (Step 1: Essential Hardware) .

Connect Peripherals

• Monitor :
• HDMI or DisplayPort: Ensure your monitor supports one of these input types and use the appropriate connecting cable.
• Resolution: If targeting image processing tasks, choose a monitor capable of displaying images at a sufficient resolution.
• Keyboard and Mouse:
• USB Ports: Most standard USB keyboards and mice will be compatible.
• Configuration: Be prepared for potential initial keyboard layout and input configuration steps later on.

Connect Power Supply

• Locate Power Port:

• Model Variations: The exact power connector varies between Jetson Nano module versions. Look for either a barrel jack connector or a micro-USB port. (Add a brief note or link indicating the different Jetson Nano module variations.)

• Plug in: Ensure a secure connection between the power supply and the Jetson Nano's power port.

• Power on: The Jetson Nano should boot up, signaled by LEDs lighting up and the initial display output on your monitor.

Initial Boot Up (without Network Connection)

• Verification: The basic boot screen indicates the power supply and boot sequence are functioning as expected. Check the board LEDs refer to your Jetson Nano model's documentation for verification.

• Network Connection Delay: This step simplifies the initial setup, reducing potential software-related conflicts.

Choose a USB camera from the compatibility list.

Connect the camera to a USB port on the Jetson Nano.

Verify camera detection in the system settings.

Follow these Steps: https://developer.nvidia.com/embedded/learn/get-started-jetson-nano-devkit

Optional

a. Install NVIDIA Jetpack SDK:

• Download the Jetpack SDK from the NVIDIA website.
• Select your Jetson Nano version and operating system.
• Follow the detailed installation instructions.
• Launch JetPack after installation and choose "Flash OS".

b. Prepare Micro-SD Card:

• Flash the Micro-SD card with the Jetpack SDK.
• Insert the Micro-SD card into the Jetson Nano.

c. Install OpenCV and TensorFlow:

• Open a terminal window.
• Update the package list: sudo apt update
• Install OpenCV: sudo apt install libopencv-dev
• Install TensorFlow: sudo pip3 install tensorflow

Get the instruction and installation here: https://github.com/VellVoid/Build-a-Face-Recognition-Machine-With-Jetson-Nano/tree/main

Optional

3. Face Recognition Implementation:

a. Prepare Dataset:

• Collect high-resolution images of various faces.
• Store the images in a folder on the Jetson Nano.

b. Develop Script:

• Implement a Python script using OpenCV for face recognition.
• The script should:

1. Load images.
2. Preprocess images.
3. Detect faces.
4. Extract features.
5. Compare features with a database.
6. Output results.

c. Training and Evaluation:

• Divide the dataset into training and testing sets.
• Train a face recognition algorithm.
• Evaluate the model's performance.

d. Optimization and Fine-tuning:

• Optimize the script and algorithm.
• Ensure the solution runs in real-time.

4. Integration and Deployment:

a. Develop User Interface:

• Implement features like live camera capture and user management.

b. Create Package:

• Package the software and resources into an installation package.
• Create installation instructions and tutorials.

Check this Video for detailed information on detection.