Local Visage

Plugins
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:placard: Summary Local Performer Recognition plugin using DeepFace.
:link: Repository https://github.com/stashapp/CommunityScripts/blob/main/plugins/LocalVisage
:information_source: Source URL https://stashapp.github.io/CommunityScripts/stable/index.yml
:open_book: Install How to install a plugin?

Local Performer Recognition

A plugin for recognizing performers from their images using DeepFace. This plugin integrates seamlessly with Stash and enables automatic facial recognition by building or updating a local model trained from your existing image collection.

:magnifying_glass_tilted_left: Features

  • Rebuild Face Recognition Model
    Completely rebuild the local facial recognition model using available images per performer.

  • Update Face Recognition Model
    Incrementally updates the model if performers have fewer images than the configured target count.

  • Automatic Server Control
    Easily start or stop the recognition server as neededā€”automatically starts when an image is queried.

  • Identify
    Click on the new icon next to an image to trigger performer identification.

:package: Requirements

  • Python 3.10.11 (temporarily, see instructions below)
  • PythonDepManager
  • StashUserscriptLibrary

:gear: Tasks

Task Description
Rebuild Face Recognition Model Fully rebuild the DeepFace model for all performers.
Update Face Recognition Model Add more images for performers with less than the target image count.
Start Server Start the local DeepFace server if itā€™s not already running.
Stop Server Gracefully stop the running recognition server.

:wrench: Settings

Setting Description
Target image count per voy Number of images to use per performer when training the model. Default is 15.

:rocket: Installation & Setup

1. Set Python Path to 3.10.11

To ensure compatibility with DeepFace and the pluginā€™s dependency resolution process:

  • Temporarily set the Python path in your system/environment to Python 3.10.11.

2. Rebuild the Model

Run the ā€œRebuild Face Recognition Modelā€ task. This will:

  • Set up a virtual environment
  • Install all necessary Python dependencies (DeepFace, etc.)
  • Build the recognition model

3. Restore Python Path (Optional)

Once setup is complete, you can revert your Python path to its original version. The plugin will continue working with the generated virtual environment.

:framed_picture: Usage

  1. Once the model is built, navigate to an image in your Stash UI.
  2. Click the Performer Recognition icon overlaying the image.
  3. The plugin will:
    • Automatically start the recognition server if itā€™s not already running
    • Query the server to identify the performer
    • Display the matched performer from the trained database
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