Face Recognition - using @smcgann/node-red-face-detection-plus

Setup Instructions for Face Recognition Flow

Initial Setup

Install the required nodes

Import the flow into the editor

Create a base folder for the training images. Example:

mkdir -p /home/pi/facerec/people/

Creating Person Profiles

Within the base folder, create individual folders for each person
Name folders as you want the person to be identified (e.g., /home/pi/facerec/people/Dave)
Add photos of each person to their respective folders
Important: Each image should contain only one face

Image Requirements

Images are internaly resized to 640x640 pixels, when detecting faces. So high-resolution images should be cropped to focus on the face (similar to passport photos). This ensures that the 640 pixels capture more relevant facial details.
Quality is more important than quantity - a few clear photos are likley to work better than many poor ones.
It's a good idea to include images from different angles, including from the actual camera in place if posible.

Operation

This flow functions as a dashboard for testing and optimizing face recognition.
When you click Enrole, it iterates through the person folders and creates flow.people which contains names, filenames and corresponding vectors. Several nodes will display status info, so you can see what is happening during this process.
If you add new images to the folders, you can run Enrole again, existing records will be skipped.
After completion any problems will be logged in flow.errors and summarised in the Valid face ? status.
If not using persistant storeage, you can configure the cosine similarity node to read from a file. Click Write to File (edit filename as required)

Troubleshooting Enrollment

If you encounter "No Face" errors:

Check image quality. (e.g. very low resolution, poor contrast etc)
Increase the Confidence Threshold in the FACE Detection enrollment node.
Try using the YoloV8s-face model

For > 1 face :

Crop images to show only one face.
Decrease the Confidence Threshold in the FACE Detection enrollment node.

Sometimes it's easier to remove problematic images rather than troubleshoot them.

To attempt to force detection, click Fix Errors

This will use the error file list and either increase or decrease threshold by 1.
After completion if there are still errors, you can click Fix Errors again.
To start fresh, click Delete ALL to remove flow.people.

Testing

Use the inject file path for testing face identification.
Any image can be sent to the recognition flow.

Reolink Doorbell Setup:

A Specific example using Reolink -

Add your doorbell's IP address and password to the flow environent variables
If using FTP:
- Configure your server for FTP access.
- Setup the camera to FTP images on person detection.
- Set the watch node path (e.g., /home/pi/FTP/files/face)
- New images arriving will trigger the flow.
If using Webhook:
- See this thread

Note: some third-party nodes may appear with blank styling, and not as they appear in the Node-RED Editor.

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So high-resolution images should be cropped to focus on the face (similar to passport photos).\n This ensures that the 640 pixels capture more relevant facial details.\n- Quality is more important than quantity - a few clear photos are likley to work better than many poor ones.\n- It's a good idea to include images from different angles, including from the actual camera in place if posible.\n---\n\n## Operation\n- This flow functions as a dashboard for testing and optimizing face recognition. \n- When you click **Enrole**, it iterates through the person folders and creates `flow.people` which contains names, filenames and corresponding vectors.\nSeveral nodes will display status info, so you can see what is happening during this process.\n- If you add new images to the folders, you can run **Enrole** again, existing records will be skipped.\n- After completion any problems will be logged in `flow.errors` and summarised in the `Valid face ?` status.\n- If not using persistant storeage, you can configure the cosine similarity node to read from a file. 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Collection Info

Face Recognition Flow

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Flow Info

Created 6 days ago

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smcgann99

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Core

change (x5)
comment (x2)
debug (x4)
delay (x1)
file (x1)
file in (x2)
function (x9)
http in (x1)
http request (x1)
http response (x1)
inject (x7)
link in (x3)
link out (x4)
switch (x1)
template (x1)
trigger (x1)
watch (x1)