Using AWS Rekognition or Azure AI Video Indexer, Curator can scan and analyse your media assets to detect faces in images and videos, automatically tagging them with metadata that identifies the specific person (personality) shown.
💡 Note: Custom face recognition is separate from built-in celebrity facial recognition. While AI services have pre-built celebrity libraries, custom face recognition allows users to upload images of any individual to train the AI model. Once trained, Curator works with the AI service to automatically tag relevant metadata to your assets.
For deeper technical details, refer to the official vendor documentation:
- Azure: Azure AI Video Indexer: customise a person model
- AWS: AWS Rekognition Collections Documentation
Prerequisites
To enable Curator to recognise and detect custom faces, a System Administrator must first install the appropriate custom face detection service plugin: Azure Custom Face Recognition (Installing Azure Custom Face Recognition) or AWS Custom Face Rekognition (Installing AWS Custom Face Rekognition). Custom facial recognition is only possible once an image library is configured using the steps below.
Reference Image Requirements
To train the AI model effectively, the ingested reference assets must meet the following criteria to ensure high-quality analysis results. If these parameters are not met, results may vary and fall short of optimal performance when detecting faces.
- Minimum Size: The face within the image must be at least 200x200 pixels.
- Maximum Resolution: The overall image resolution should not exceed 1920x1080.
- Single Subject: The image must not contain any other faces.
- Full Framing: The image should include the person’s entire head.
- Orientation: The subject must be facing forward.
Creating a Custom Face Image Library
1. Ingest Reference Images
Ingest the high-quality reference image(s) of the individual into Curator.
2. Organise into Collections
- For AWS: Add the image(s) to a collection. We recommend naming the collection after the person (e.g.,
Jeremy Clarkson).
- For Azure: Add the image(s) to a collection and name it
Default(unless your administrator has configured a different name). - Note: This single Azure collection can host all your faces and associate them with different personalities.
3. Update Asset Metadata
Select the assets within your collection and update the specific metadata fields based on your cloud provider:
- For AWS: Edit the metadata field
FaceCollectionLabeland assign the personality’s name (e.g.,Jeremy Clarkson). - For Azure: Edit the metadata field
AzureCustomFacePersonNameand assign the personality’s name (e.g.,Jeremy Clarkson).
4. Trigger the AI Training Scan
- Right-click the collection.
- Select your relevant face recognition service plugin.
- Choose the scan option for your provider: Scan Collection to Update Facial Rekognition Library (AWS) or Create or Update Azure Custom Face Library (Azure). (Note: Your exact UI plugin options may vary slightly based on your system configuration as shown below)
AWS Plugin Menu
Azure Plugin Menu
5. Verification
Curator will push the FaceCollectionLabel (AWS) or AzureCustomFacePersonName (Azure) metadata to the cloud service.
Once the faces have been confirmed to the library the metadata on the assets within the collection will be updated. FaceCollectionAdded will be updated to True
Once the metadata is set True (Approx 1-3 min), you're all set to begin sending assets for custom face recognition!
Next Steps
To run facial recognition scans on your assets using your newly trained library, please refer to the Custom Face Detection and Celebrity Recognition guide.