edit pass: cognitive-services-face-articles-batch1

Author: paulth1

articles/cognitive-services/Face/Face-API-How-to-Topics/HowtoIdentifyFacesinImage.md

@@ -52,7 +52,7 @@ In this step, a PersonGroup named "MyFriends" contains Anna, Bill, and Clare. Ea
 
 ![MyFriends](../Images/group.image.1.jpg)
 
-### 2.1 Define people for the PersonGroup
+### Step 2.1: Define people for the PersonGroup
 A person is a basic unit of identify. A person can have one or more known faces registered. A PersonGroup is a collection of people. Each person is defined within a particular PersonGroup. Identification is done against a PersonGroup. The task is to create a PersonGroup, and then create the people in it, such as Anna, Bill, and Clare.
 
 First, create a new PersonGroup by using the [PersonGroup - Create](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395244) API. The corresponding client library API is the CreatePersonGroupAsync method for the FaceServiceClient class. The group ID that's specified to create the group is unique for each subscription. You also can get, update, or delete PersonGroups by using other PersonGroup APIs. 
@@ -74,7 +74,7 @@ CreatePersonResult friend1 = await faceServiceClient.CreatePersonAsync(
 
 // Define Bill and Clare in the same way
 ```
-### <a name="step2-2"></a> 2.2 Detect faces and register them to the correct person
+### <a name="step2-2"></a> Step 2.2: Detect faces and register them to the correct person
 Detection is done by sending a "POST" web request to the [Face - Detect](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236) API with the image file in the HTTP request body. When you use the client library, face detection is done through the DetectAsync method for the FaceServiceClient class.
 
 For each face that's detected, call [PersonGroup Person – Add Face](https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f3039523b) to add it to the correct person.

articles/cognitive-services/Face/Face-API-How-to-Topics/how-to-use-large-scale.md

@@ -207,7 +207,7 @@ As is shown in `TrainLargeFaceList()`, there's a time interval in milliseconds t
 
 The same strategy also applies to LargePersonGroup. For example, when you train a LargePersonGroup with 1 million persons, `timeIntervalInMilliseconds` might be 60,000, which is a 1-minute interval.
 
-## Step 3.2 Small-scale buffer
+## Step 3.2: Small-scale buffer
 
 Persons or faces in a LargePersonGroup or a LargeFaceList are searchable only after being trained. In a dynamic scenario, new persons or faces are constantly added and must be immediately searchable, yet training might take longer than desired. 
 
@@ -222,7 +222,7 @@ An example workflow:
 1. When the buffer collection size increases to a threshold or at a system idle time, create a new buffer collection. Trigger the Train operation on the master collection.
 1. Delete the old buffer collection after the Train operation finishes on the master collection.
 
-## Step 3.3 Standalone training
+## Step 3.3: Standalone training
 
 If a relatively long latency is acceptable, it isn't necessary to trigger the Train operation right after you add new data. Instead, the Train operation can be split from the main logic and triggered regularly. This strategy is suitable for dynamic scenarios with acceptable latency. It can be applied to static scenarios to further reduce the Train frequency.