Merge pull request #201606 from PatrickFarley/face-gate2

[cog svcs] Face gating

Author: v-ccolin

articles/cognitive-services/Computer-vision/concept-face-detection.md

@@ -15,6 +15,8 @@ ms.author: pafarley
 
 # Face detection and attributes
 
+[!INCLUDE [Gate notice](./includes/identity-gate-notice.md)]
+
 This article explains the concepts of face detection and face attribute data. Face detection is the process of locating human faces in an image and optionally returning different kinds of face-related data.
 
 You use the [Face - Detect](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236) API to detect faces in an image. To get started using the REST API or a client SDK, follow a [quickstart](./quickstarts-sdk/identity-client-library.md). Or, for a more in-depth guide, see [Call the detect API](./how-to/identity-detect-faces.md).
@@ -39,6 +41,8 @@ The Detection_03 model currently has the most accurate landmark detection. The e
 
 ## Attributes
 
+[!INCLUDE [Sensitive attributes notice](./includes/identity-sensitive-attributes.md)]
+
 Attributes are a set of features that can optionally be detected by the [Face - Detect](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236) API. The following attributes can be detected:
 
 * **Accessories**. Whether the given face has accessories. This attribute returns possible accessories including headwear, glasses, and mask, with confidence score between zero and one for each accessory.

articles/cognitive-services/Computer-vision/how-to/add-faces.md

@@ -17,6 +17,8 @@ ms.custom: devx-track-csharp
 
 # Add faces to a PersonGroup
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 This guide demonstrates how to add a large number of persons and faces to a PersonGroup object. The same strategy also applies to LargePersonGroup, FaceList, and LargeFaceList objects. This sample is written in C# by using the Azure Cognitive Services Face .NET client library.
 
 ## Step 1: Initialization

articles/cognitive-services/Computer-vision/how-to/find-similar-faces.md

@@ -16,6 +16,8 @@ ms.custom:
 
 # Find similar faces
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 The Find Similar operation does face matching between a target face and a set of candidate faces, finding a smaller set of faces that look similar to the target face. This is useful for doing a face search by image.
 
 This guide demonstrates how to use the Find Similar feature in the different language SDKs. The following sample code assumes you have already authenticated a Face client object. For details on how to do this, follow a [quickstart](../quickstarts-sdk/identity-client-library.md).

articles/cognitive-services/Computer-vision/how-to/identity-analyze-video.md

@@ -17,6 +17,8 @@ ms.custom: devx-track-csharp
 
 # Example: How to Analyze Videos in Real-time
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 This guide will demonstrate how to perform near-real-time analysis on frames taken from a live video stream. The basic components in such a system are:
 
 - Acquire frames from a video source

articles/cognitive-services/Computer-vision/how-to/identity-detect-faces.md

@@ -17,6 +17,10 @@ ms.custom: devx-track-csharp
 
 # Call the Detect API
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
+[!INCLUDE [Sensitive attributes notice](../includes/identity-sensitive-attributes.md)]
+
 This guide demonstrates how to use the face detection API to extract attributes like age, emotion, or head pose from a given image. You'll learn the different ways to configure the behavior of this API to meet your needs.
 
 The code snippets in this guide are written in C# by using the Azure Cognitive Services Face client library. The same functionality is available through the [REST API](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236).

articles/cognitive-services/Computer-vision/how-to/specify-recognition-model.md

@@ -16,6 +16,8 @@ ms.custom: devx-track-csharp
 
 # Specify a face recognition model
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 This guide shows you how to specify a face recognition model for face detection, identification and similarity search using the Azure Face service.
 
 The Face service uses machine learning models to perform operations on human faces in images. We continue to improve the accuracy of our models based on customer feedback and advances in research, and we deliver these improvements as model updates. Developers can specify which version of the face recognition model they'd like to use. They can choose the model that best fits their use case.

articles/cognitive-services/Computer-vision/how-to/use-large-scale.md

@@ -17,6 +17,8 @@ ms.custom: devx-track-csharp
 
 # Example: Use the large-scale feature
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 This guide is an advanced article on how to scale up from existing PersonGroup and FaceList objects to LargePersonGroup and LargeFaceList objects, respectively. This guide demonstrates the migration process. It assumes a basic familiarity with PersonGroup and FaceList objects, the [Train](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/599ae2d16ac60f11b48b5aa4) operation, and the face recognition functions. To learn more about these subjects, see the [face recognition](../concept-face-recognition.md) conceptual guide.
 
 LargePersonGroup and LargeFaceList are collectively referred to as large-scale operations. LargePersonGroup can contain up to 1 million persons, each with a maximum of 248 faces. LargeFaceList can contain up to 1 million faces. The large-scale operations are similar to the conventional PersonGroup and FaceList but have some differences because of the new architecture. 

articles/cognitive-services/Computer-vision/how-to/use-persondirectory.md

@@ -17,6 +17,8 @@ ms.custom: [devx-track-csharp, cogserv-non-critical-vision]
 
 # Use the PersonDirectory structure
 
+[!INCLUDE [Gate notice](../includes/identity-gate-notice.md)]
+
 To perform face recognition operations such as Identify and Find Similar, Face API customers need to create an assorted list of **Person** objects. The new **PersonDirectory** is a data structure that contains unique IDs, optional name strings, and optional user metadata strings for each **Person** identity added to the directory.
 
 Currently, the Face API offers the **LargePersonGroup** structure, which has similar functionality but is limited to 1 million identities. The **PersonDirectory** structure can scale up to 75 million identities.

articles/cognitive-services/Computer-vision/includes/identity-gate-notice.md

@@ -0,0 +1,14 @@
+---
+title: Face gate notice
+titleSuffix: Azure Cognitive Services
+services: cognitive-services
+author: PatrickFarley
+manager: nitinme
+ms.service: cognitive-services
+ms.topic: include 
+ms.date: 06/14/2022
+ms.author: pafarley
+---
+
+> [!IMPORTANT]
+> 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.

articles/cognitive-services/Computer-vision/includes/identity-sensitive-attributes.md

@@ -0,0 +1,15 @@
+---
+title: Face sensitive attributes notice
+titleSuffix: Azure Cognitive Services
+services: cognitive-services
+author: PatrickFarley
+manager: nitinme
+ms.service: cognitive-services
+ms.topic: include 
+ms.date: 06/14/2022
+ms.author: pafarley
+---
+
+> [!IMPORTANT]
+> Microsoft will be retiring facial recognition capabilities that can be used to try to infer emotional states and internal identity attributes which, if misused, can subject people to stereotyping, discrimination or unfair denial of services. These include capabilities that predict emotion, gender, age, smile, facial hair, hair and makeup. Existing customers have until June 21, 2023 to discontinue use of these capabilities before they are retired. Read more about this decision [here](https://azure.microsoft.com/blog/responsible-ai-investments-and-safeguards-for-facial-recognition/).
+