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articles/ai-services/computer-vision/how-to/identity-access-token.md

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```bash
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curl -X POST 'https://<isv-endpoint>/sts/v1.0/issueLimitedAccessToken?expiredTime=3600' \
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-H 'Ocp-Apim-Subscription-Key: <client-face-key>' \
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-H 'Ocp-Apim-Subscription-Key: <isv-face-key>' \
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-H 'Content-Type: application/json' \
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-d '{
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"resourceId": "/subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.CognitiveServices/accounts/<face-resource-name>",
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Then, make the following changes:
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1. Replace `<isv-endpoint>` with the endpoint of the ISV's resource. For example, **westus.api.cognitive.microsoft.com**.
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1. Optionally set the `expiredTime` parameter to set the expiration time of the token in seconds. It must be between 60 and 86400. The default value is 3600 (one hour).
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1. Replace `<client-face-key>` with the key of the client's Face resource.
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1. Replace `<isv-face-key>` with the key of the ISV's Face resource.
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1. Replace `<subscription-id>` with the subscription ID of the client's Azure subscription.
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1. Replace `<resource-group-name>` with the name of the client's resource group.
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1. Replace `<face-resource-name>` with the name of the client's Face resource.

articles/ai-services/computer-vision/how-to/specify-detection-model.md

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In this article, you learned how to specify the detection model to use with different Face APIs. Next, follow a quickstart to get started with face detection and analysis.
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* [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp%253fpivots%253dprogramming-language-csharp)
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* [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python%253fpivots%253dprogramming-language-python)
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* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java%253fpivots%253dprogramming-language-java)
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* [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript%253fpivots%253dprogramming-language-javascript)
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* [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp)
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* [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python)
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* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java)
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* [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript)
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[Detect]: /rest/api/face/face-detection-operations/detect
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[Identify From Person Group]: /rest/api/face/face-recognition-operations/identify-from-person-group

articles/ai-services/computer-vision/how-to/specify-recognition-model.md

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In this article, you learned how to specify the recognition model to use with different Face service APIs. Next, follow a quickstart to get started with face detection.
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* [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp%253fpivots%253dprogramming-language-csharp)
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* [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python%253fpivots%253dprogramming-language-python)
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* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java%253fpivots%253dprogramming-language-java)
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* [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript%253fpivots%253dprogramming-language-javascript)
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* [Face .NET SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-csharp)
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* [Face Python SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-python)
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* [Face Java SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-java)
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* [Face JavaScript SDK](../quickstarts-sdk/identity-client-library.md?pivots=programming-language-javascript)
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[Detect]: /rest/api/face/face-detection-operations/detect
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[Verify Face To Face]: /rest/api/face/face-recognition-operations/verify-face-to-face

articles/ai-services/computer-vision/includes/identity-gate-notice.md

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---
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> [!CAUTION]
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> Face service access is limited based on eligibility and usage criteria in order to support our Responsible AI principles. Face service is only available to Microsoft managed customers and partners. Use the [Face Recognition intake form](https://aka.ms/facerecognition) to apply for access. For more information, see the [Face limited access](/legal/cognitive-services/computer-vision/limited-access-identity?context=%2Fazure%2Fcognitive-services%2Fcomputer-vision%2Fcontext%2Fcontext) page.
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> Face service access is limited based on eligibility and usage criteria in order to support our Responsible AI principles. Face service is only available to Microsoft managed customers and partners. Use the [Face Recognition intake form](https://aka.ms/facerecognition) to apply for access. For more information, see the [Face limited access](/legal/cognitive-services/computer-vision/limited-access-identity?context=%2Fazure%2Fai-services%2Fcomputer-vision%2Fcontext%2Fcontext) page.

articles/ai-services/computer-vision/reference-face-error-codes.md

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|Bad Request (400)|InvalidImageSize|Image size is too small.|The valid image file size should be larger than or equal to 1 KB.|
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|Bad Request (400)|InvalidImageSize|Image size is too big.|The valid image file size should be no larger than 6 MB.|
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|Unauthorized (401)|401|Access denied due to invalid subscription key or wrong API endpoint. Make sure to provide a valid key for an active subscription and use a correct regional API endpoint for your resource.||
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|Forbidden (403)|UnsupportedFeature|Feature is not supported, missing approval for one or more of the following features: Identification, Verification.|Register for access by submitting a [registration form](https://aka.ms/facerecognition).|
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|Conflict (409)|ConcurrentOperationConflict|There is a conflict operation on resource `<resourceName>`, please try later.||
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|Too Many Requests (429)|429|Rate limit is exceeded.||
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|Bad Request (400)|BadArgument|Invalid argument returnFaceAttributes.||
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|Bad Request (400)|BadArgument|'returnFaceAttributes' is not supported by detection_02.||
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|Bad Request (400)|BadArgument|'returnLandmarks' is not supported by detection_02.||
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|Forbidden (403)|UnsupportedFeature|Return Attributes (emotion, gender, age, smile, facial hair, hair and makeup) have been deprecated and are no longer supported.|Read more about this decision [here](https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/).|
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## Face Liveness Session error codes
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Next steps
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- [Face API reference](/rest/api/face/operation-groups)
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- [Face API reference](/rest/api/face/operation-groups)

articles/ai-services/computer-vision/tutorials/liveness.md

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Once you have access to the SDK, follow instructions in the [azure-ai-vision-sdk](https://github.com/Azure-Samples/azure-ai-vision-sdk) GitHub repository to integrate the UI and the code into your native mobile application. The liveness SDK supports Java/Kotlin for Android mobile applications, Swift for iOS mobile applications and JavaScript for web applications:
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- For Swift iOS, follow the instructions in the [iOS sample](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-ios-readme)
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- For Kotlin/Java Android, follow the instructions in the [Android sample](https://aka.ms/liveness-sample-java)
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- For JavaScript Web, follow the instructions in the [Web sample](https://aka.ms/liveness-sample-web)
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- For Kotlin/Java Android, follow the instructions in the [Android sample](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-android-readme)
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- For JavaScript Web, follow the instructions in the [Web sample](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-web-readme)
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Once you've added the code into your application, the SDK handles starting the camera, guiding the end-user in adjusting their position, composing the liveness payload, and calling the Azure AI Face cloud service to process the liveness payload.
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To learn about other options in the liveness APIs, see the Azure AI Vision SDK reference.
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- [Kotlin (Android)](https://aka.ms/liveness-sample-java)
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- [Kotlin (Android)](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-android-readme)
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- [Swift (iOS)](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-ios-readme)
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- [JavaScript (Web)](https://aka.ms/azure-ai-vision-face-liveness-client-sdk-web-readme)
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articles/ai-services/content-moderator/video-moderation-api.md

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### Create an Azure Media Services account
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Follow the instructions in [Create an Azure Media Services account](/azure/media-services/previous/media-services-portal-create-account) to subscribe to AMS and create an associated Azure storage account. In that storage account, create a new Blob storage container.
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Follow the instructions in [Create an Azure Media Services account](/azure/media-services/latest/account-create-how-to) to subscribe to AMS and create an associated Azure storage account. In that storage account, create a new Blob storage container.
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<a name='create-an-azure-active-directory-application'></a>
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Select your app registration and click the **Manage application** button below it. Note the value in the **Application ID** field; you will need this later. Select **Settings** > **Keys**, and enter a description for a new key (such as "VideoModKey"). Select **Save**, and then notice the new key value. Copy this string and save it somewhere secure.
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For a more thorough walkthrough of the above process, See [Get started with Microsoft Entra authentication](/azure/media-services/previous/media-services-portal-get-started-with-aad).
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For a more thorough walkthrough of the above process, See [Get started with Microsoft Entra authentication](/azure/media-services/latest/access-api-howto).
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Once you've done this, you can use the video moderation media processor in two different ways.
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articles/ai-services/document-intelligence/versioning/v3-1-migration-guide.md

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## Changes to the REST API endpoints
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The v3.1 REST API combines the analysis operations for layout analysis, prebuilt models, and custom models into a single pair of operations by assigning **`documentModels`** and **`modelId`** to the layout analysis (../prebuilt-layout) and prebuilt models.
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The v3.1 REST API combines the analysis operations for layout analysis, prebuilt models, and custom models into a single pair of operations by assigning **`documentModels`** and **`modelId`** to the [layout analysis](../prebuilt/layout.md) and prebuilt models.
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### POST request
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articles/ai-services/language-service/personally-identifiable-information/concepts/conversations-entity-categories.md

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Any numeric or alphanumeric identifier that could contain any PII information.
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* Case Number
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* Driver's license
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* Medicare Beneficiary Identifier (MBI)
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* Member Number
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* Ticket number
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* Bank account number

articles/ai-services/language-service/question-answering/tutorials/active-learning.md

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> * Accept/reject active learning suggestions
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> * Add alternate questions
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This tutorial shows you how to enhance your custom question answering project with active learning. If you notice that customers are asking questions, which are not part of your project. There are often variations of questions that are paraphrased differently.
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This tutorial shows you how to enhance your custom question answering project with active learning. If you notice that customers are asking questions that are not covered in your project, they may be paraphrased variations of questions.
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These variations when added as alternate questions to the relevant question answer pair, help to optimize the project to answer real world user queries. You can manually add alternate questions to question answer pairs through the editor. At the same time, you can also use the active learning feature to generate active learning suggestions based on user queries. The active learning feature, however, requires that the project receives regular user traffic to generate suggestions.
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These variations, when added as alternate questions to the relevant question answer pair, help to optimize the project to answer real world user queries. You can manually add alternate questions to question answer pairs through the editor. At the same time, you can also use the active learning feature to generate active learning suggestions based on user queries. The active learning feature, however, requires that the project receives regular user traffic to generate suggestions.
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## Use active learning
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