| title | titleSuffix | description | author | manager | ms.service | ms.subservice | ms.custom | ms.topic | ms.date | ms.author | |
|---|---|---|---|---|---|---|---|---|---|---|---|
Face recognition - Face |
Azure AI services |
Learn the concept of Face recognition, its related operations, and the underlying data structures. |
PatrickFarley |
nitinme |
azure-ai-vision |
azure-ai-face |
|
conceptual |
02/14/2024 |
pafarley |
This article explains the concept of Face recognition, its related operations, and the underlying data structures. Broadly, face recognition is the process of verifying or identifying individuals by their faces. Face recognition is important in implementing the identification scenario, which enterprises and apps can use to verify that a (remote) user is who they claim to be.
You can try out the capabilities of face recognition quickly and easily using Vision Studio.
[!div class="nextstepaction"] Try Vision Studio
[!INCLUDE Gate notice]
You need to create a PersonGroup or LargePersonGroup to store the set of people to match against. PersonGroups hold Person objects, which each represent an individual person and hold a set of face data belonging to that person.
The Train operation prepares the data set to be used in face data comparisons.
The Identify operation takes one or several source face IDs (from a DetectedFace or PersistedFace object) and a PersonGroup or LargePersonGroup. It returns a list of the Person objects that each source face might belong to. Returned Person objects are wrapped as Candidate objects, which have a prediction confidence value.
The Verify operation takes a single face ID (from a DetectedFace or PersistedFace object) and a Person object. It determines whether the face belongs to that same person. Verification is one-to-one matching and can be used as a final check on the results from the Identify API call. However, you can optionally pass in the PersonGroup to which the candidate Person belongs to improve the API performance.
The recognition operations use mainly the following data structures. These objects are stored in the cloud and can be referenced by their ID strings. ID strings are always unique within a subscription, but name fields may be duplicated.
See the Face recognition data structures guide.
Use the following tips to ensure that your input images give the most accurate recognition results:
[!INCLUDE identity-input-technical] [!INCLUDE identity-input-composition]
- You can utilize the
qualityForRecognitionattribute in the face detection operation when using applicable detection models as a general guideline of whether the image is likely of sufficient quality to attempt face recognition on. Only"high"quality images are recommended for person enrollment and quality at or above"medium"is recommended for identification scenarios.
Now that you're familiar with face recognition concepts, Write a script that identifies faces against a trained PersonGroup.