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articles/ai-services/computer-vision/concept-face-recognition.md

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@@ -10,7 +10,7 @@ ms.subservice: azure-ai-face
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ms.custom:
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- ignite-2023
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ms.topic: conceptual
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ms.date: 10/16/2024
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ms.date: 05/01/2025
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ms.author: pafarley
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feedback_help_link_url: https://learn.microsoft.com/answers/tags/156/azure-face
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[!INCLUDE [identity-input-composition](includes/identity-input-composition.md)]
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* You can use the `qualityForRecognition` attribute in the [face detection](./how-to/identity-detect-faces.md) 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.
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## Next steps
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## Next step
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Now that you're familiar with face recognition concepts, Write a script that identifies faces against a trained PersonGroup.
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* [Face quickstart](./quickstarts-sdk/identity-client-library.md)
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> [!div class="nextstepaction"]
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> [Face quickstart](./quickstarts-sdk/identity-client-library.md)

articles/ai-services/computer-vision/concept-object-detection-40.md

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ms.service: azure-ai-vision
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ms.topic: conceptual
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ms.date: 10/31/2024
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ms.date: 05/01/2025
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## Limitations
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It's important to note the limitations of object detection so you can avoid or mitigate the effects of false negatives (missed objects) and limited detail.
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Note the limitations of object detection so you can avoid or mitigate the effects of false negatives (missed objects) and limited detail.
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* Objects are generally not detected if they're small (less than 5% of the image).
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* Objects are generally not detected if they're arranged closely together (a stack of plates, for example).

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