Merge pull request #265050 from PatrickFarley/comvis-updates

Comvis updates

Author: prmerger-automator[bot]

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

@@ -19,6 +19,9 @@ ms.author: pafarley
 
 [!INCLUDE [Gate notice](./includes/identity-gate-notice.md)]
 
+> [!IMPORTANT]
+> Face attributes are predicted through the use of statistical algorithms. They might not always be accurate. Use caution when you make decisions based on attribute data. Please refrain from using these attributes for anti-spoofing. Instead, we recommend using Face Liveness detection. For more information, please refer to [Tutorial: Detect liveness in faces](/azure/ai-services/computer-vision/tutorials/liveness). 
+
 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).
@@ -67,9 +70,6 @@ Attributes are a set of features that can optionally be detected by the [Face -
     >[!NOTE]
     > The availability of each attribute depends on the detection model specified. QualityForRecognition attribute also depends on the recognition model, as it is currently only available when using a combination of detection model detection_01 or detection_03, and recognition model recognition_03 or recognition_04.
 
-> [!IMPORTANT]
-> Face attributes are predicted through the use of statistical algorithms. They might not always be accurate. Use caution when you make decisions based on attribute data.
-
 ## Input data
 
 Use the following tips to make sure that your input images give the most accurate detection results:

articles/ai-services/computer-vision/how-to/use-headpose.md

@@ -20,6 +20,9 @@ ms.custom:
 
 In this guide, you'll see how you can use the HeadPose attribute of a detected face to enable some key scenarios.
 
+> [!IMPORTANT]
+> Face attributes are predicted through the use of statistical algorithms. They might not always be accurate. Use caution when you make decisions based on attribute data. Please refrain from using these attributes for anti-spoofing. Instead, we recommend using Face Liveness detection. For more information, please refer to [Tutorial: Detect liveness in faces](/azure/ai-services/computer-vision/tutorials/liveness). 
+
 ## Rotate the face rectangle
 
 The face rectangle, returned with every detected face, marks the location and size of the face in the image. By default, the rectangle is always aligned with the image (its sides are vertical and horizontal); this can be inefficient for framing angled faces. In situations where you want to programmatically crop faces in an image, it's better to be able to rotate the rectangle to crop.