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Generate 128D face embeddings for each detected face
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Tool usage guide
The AI Face Descriptors tool generates 128-dimensional face embeddings for each detected face in an uploaded image. These embeddings are numerical representations of facial features, ideal for face recognition, verification, and analysis tasks.
Background
A company wants to implement facial recognition for employee login to secure systems.
Problem
Need to generate accurate face embeddings from employee ID photos to create a verification database.
How to use
Upload employee photos, set minConfidence to 0.8 for high accuracy, and process each image to extract embeddings.
Outcome
Reliable face embeddings are obtained and stored for real-time verification during login attempts.
Background
After a conference, hundreds of photos need to be sorted by attendee for quick sharing.
Problem
Manually tagging faces in each photo is time-consuming and prone to errors.
How to use
Batch upload all event photos, use default settings to detect faces, and extract embeddings for clustering.
Outcome
Faces are grouped based on similar embeddings, enabling efficient tagging and search across the photo collection.
A face embedding is a 128-dimensional vector that numerically represents facial features, used for comparison in recognition tasks.
The tool supports common image formats such as JPEG, PNG, and others as specified in the upload interface.
Set minConfidence between 0.05 and 0.99 to filter detections; higher values reduce false positives by requiring more certainty.
Yes, it detects all faces by default, or you can set maxResults to limit the number of faces processed.
The output is a JSON array containing objects for each detected face, including the 128D embedding and detection metadata.