Spelling & Typo Corrections

identity-verification/module_1/1.0 SETUP - Gather images and setup S3 buckets for IDV modules.ipynb

@@ -87,10 +87,7 @@
    "source": [
     "s3 = boto3.client('s3')\n",
     "try:\n",
-    "   s3.create_bucket(\n",
-    "       Bucket=bucket_name,\n",
-    "       CreateBucketConfiguration={'LocationConstraint': aws_region}\n",
-    "   )\n",
+    "    s3.create_bucket(Bucket=bucket_name)\n",
     "except Exception as err:\n",
     "    print(\"ERROR: {}\".format(err))"
    ]

identity-verification/module_1/1.1 Getting Started.ipynb

@@ -93,7 +93,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# there should be close to 1000 files in the bucket. \n",
     "def list_s3_files_using_client(bucket_name):\n",
     "    \"\"\"\n",
     "    This functions list all files in s3 bucket.\n",

identity-verification/module_2/2.1 Rekognition IDV API Examples.ipynb

@@ -14,7 +14,7 @@
     "\n",
     "-------\n",
     "\n",
-    "In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning powered facial biometrics can enable online user identity verification. Amazon Rekognition offers pre-trained facial recognition and analysis capabilities that you can quickly add to your user onboarding and authentication work flows to verify opted-in users' identity online. \n",
+    "In-person user identity verification is slow to scale, costly, and high friction for users. Machine learning powered facial biometrics can enable online user identity verification. Amazon Rekognition offers pre-trained facial recognition and analysis capabilities that you can quickly add to your user onboarding and authentication workflows to verify opted-in users' identity online. \n",
     "\n",
     "In this notebook, we'll use the Amazon Rekgonition's key APIs for Identity Verification. After running this notebook you should be able to use the following APIs:\n",
     "\n",
@@ -317,7 +317,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## IndexFaces \n",
+    "## Index Multiple Faces \n",
     "-----\n",
     "\n",
     "The easy way to index multiple faces is to simply make a list of faces and loop over the list, in this case we are going to use the \"reference name\" as our external image id and the reference image will be the image we add to our collection. Of course you can use a variety of parallel processing methods to speed up indexing operations, however in this case here we'll keep it simple and just use a for loop. Note we are using about 100 images so this may take a few seconds. \n",
@@ -477,7 +477,7 @@
     "----\n",
     "Pass the FaceID above to search_faces, this will identify potential matches within the collection. \n",
     "\n",
-    "**Select a face id from the results above and enter it below**"
+    "**Select a face id from the results above and enter it below** "
    ]
   },
   {
@@ -486,7 +486,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "face_id = \"\" # replace with your own face ID\n",
     "try:\n",
     "    response = rek_client.search_faces(\n",
@@ -664,10 +663,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = rek_client.delete_collection(\n",
-    "   CollectionId= collection_name\n",
-    ")\n",
-    "response"
+    "try:\n",
+    "    rek_client.delete_collection(\n",
+    "        CollectionId=collection_name\n",
+    "    )\n",
+    "except Exception as err:\n",
+    "    print(\"ERROR: {}\".format(err))"
    ]
   }
  ],

identity-verification/module_2/2.2 Onboarding IDV API Example.ipynb

@@ -95,7 +95,7 @@
     "## Name your Collection \n",
     "\n",
     "# -- onboarded users collection --\n",
-    "collection_name = '' # name your collection something like \"onboarded-users\"\n",
+    "collection_name = ' ' # name your collection something like \"onboarded-users\"\n",
     "\n",
     "try:\n",
     "    rek_client.create_collection(\n",
@@ -279,7 +279,14 @@
     "    'Bucket':bucket_name,\n",
     "    'Name':id_image}},\n",
     "                                   Attributes=['ALL'])\n",
-    "response"
+    "print(\"-- 1st face found:\")\n",
+    "print(response['FaceDetails'][0]['BoundingBox'])\n",
+    "print(response['FaceDetails'][0]['Quality'])\n",
+    "\n",
+    "print(\"-- 2nd face found:\")\n",
+    "print(response['FaceDetails'][1]['BoundingBox'])\n",
+    "print(response['FaceDetails'][1]['Quality'])\n",
+    "\n"
    ]
   },
   {
@@ -324,7 +331,7 @@
     "'Quality': {'Brightness': 94.17919921875, 'Sharpness': 46.02980041503906}\n",
     "    \n",
     "# -- selfie quality --\n",
-    "'Quality': {'Brightness': 93.77082824707031,'Sharpness': 20.927310943603516}\n",
+    "'Quality': {'Brightness': 89.2042007446289, 'Sharpness': 53.330047607421875}\n",
     "```"
    ]
   },
@@ -339,7 +346,7 @@
     "    'Bucket':bucket_name,\n",
     "    'Name':selfie_image}},\n",
     "                                   Attributes=['ALL'])\n",
-    "response"
+    "print(response['FaceDetails'][0]['Quality'])"
    ]
   },
   {
@@ -545,7 +552,7 @@
    "source": [
     "## Clean up\n",
     "----\n",
-    "Here we simply need to delete our collections \n"
+    "As part of our cleanup, we can delete our two collections. This will delete the collections and all the face vectors contained within.\n"
    ]
   },
   {
@@ -560,17 +567,18 @@
     "    rek_client.delete_collection(\n",
     "        CollectionId=collection_name\n",
     "    )\n",
-    "except:\n",
-    "    print(\"collection: {} NOT FOUND\".format(collection_name))\n",
     "\n",
+    "except Exception as err:\n",
+    "    print(\"ERROR: {}\".format(err))\n",
+    "    \n",
     "# fraudulent user collection \n",
     "\n",
     "try:\n",
     "    rek_client.delete_collection(\n",
     "        CollectionId=fraud_collection_name\n",
     "    )\n",
-    "except:\n",
-    "     print(\"collection: {} NOT FOUND\".format(fraud_collection_name))"
+    "except Exception as err:\n",
+    "    print(\"ERROR: {}\".format(err))"
    ]
   }
  ],

identity-verification/module_2/2.3 Authentication IDV API Example.ipynb

@@ -193,7 +193,7 @@
     "----\n",
     "\n",
     "Here we want to do some basic checks:\n",
-    "1. that we can detect that there is only one face in the selie \n",
+    "1. that we can detect that there is only one face in the selfie \n",
     "2. the quality (sharpness and brightness) are sufficient to match with \n",
     "\n",
     "Note: we could do several other checks, but we'll see those in module 3.\n",
@@ -347,7 +347,7 @@
     "<div class=\"alert alert-info\"><strong> Results </strong>\n",
     "    \n",
     "\n",
-    "- Check out the similarity of each face found, it should range from 99.99 to 99.96 (Rekognition is extreemly accurate) \n",
+    "- Check out the similarity of each face found, it should range from 99.99 to 99.96  \n",
     "- Also note the ExternalImageId of each face, they should all match Entity_X_Cannon\n",
     "    \n",
     "</div>"
@@ -391,7 +391,7 @@
     "## Search within Collection\n",
     "----- \n",
     "\n",
-    "What faces matches another face within the collection? well now we have several faces of Al Gore lets see what that looks like."
+    "What faces matches another face within the collection? We now have several faces of the same person, lets see what that looks like."
    ]
   },
   {
@@ -400,8 +400,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "## Snag a FaceID from above\n",
-    "face_id = \"628d65f7-e96b-45fb-91f7-3841064c6fa7\"\n",
+    "## -- Snag a FaceID from above --\n",
+    "face_id = \" \" # enter FaceID from above\n",
     "try:\n",
     "    response = rek_client.search_faces(\n",
     "        CollectionId=collection_name,\n",
@@ -420,7 +420,7 @@
     "## Clean up\n",
     "------\n",
     "\n",
-    "now simply delete our collection "
+    "As part of our cleanup, we can delete our collection. This will delete the collection and all the face vectors contained within."
    ]
   },
   {
@@ -429,10 +429,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "response = rek_client.delete_collection(\n",
-    "   CollectionId= collection_name\n",
-    ")\n",
-    "response"
+    "try:\n",
+    "    rek_client.delete_collection(\n",
+    "        CollectionId=collection_name\n",
+    "    )\n",
+    "except Exception as err:\n",
+    "    print(\"ERROR: {}\".format(err))"
    ]
   }
  ],