The document discusses AWS Rekognition facial recognition services. It provides steps to set up an application using Rekognition, including creating a collection to store faces, uploading images to S3, detecting and indexing faces, searching for matches, and managing the face data. Rekognition allows analyzing images to extract metadata like faces and facial features which can then be used to identify individuals by matching new images to the stored facial images.

1. AWSome Day 2018
AWS Rekognition -
Riconoscimento Facciale
PUG Torino - Marzo 2019

2. Walter Dal Mut
Solution Architect - AWS Certified
SysOps Administrator - AWS Certified
Docker Certified Associate
@walterdalmut
walter.dalmut@corley.it
corley.it

3. 28 Marzo 2019
Codice Sconto: pug-sconto

4. Scalability and cost optimization with Container Services
CloudConf2019 - General
Setup
AWS Rekognition
Summary

5. Get the APP
https://u.corley.it/s/b4

6. ➢ Receive the CloudConf email with the web
application endpoint
➢ The barcode is used on the web application
➢ The user upload its own picture connecting the
identification number
➢ The pictures is checked to drop common mistakes
○ Drop multiple users (too many people)
○ Dark or too bright pictures
○ Low quality pictures
○ etc.
■ We believe that users are somehow polite
➢ AWS Rekognition tear apart the picture in favor of
raw information
○ (no images are stored)
The user upload its own picture

7. ➢ Locally runs a simple face recognition algorithm
➢ When a face is detected a frame is captured
➢ The captured frame is processed by AWS
Rekognition Service
➢ The External Image Identification number is
used to get the user information
➢ The user information is stored as PDF file
➢ The file is printed using a thermal printer
Local algorithm

8. Local configuration

9. AWS Rekognition
$ aws rekognition list-collections
{
"CollectionIds": [
"collection-10",
"corley-test"
],
"FaceModelVersions": [
"4.0",
"4.0"
]
}

10. AWS Rekognition
$ aws rekognition create-collection --collection-id collection-11
{
"StatusCode": 200,
"CollectionArn":
"aws:rekognition:eu-west-1:xxxxxxxxxxxx:collection/collection-11",
"FaceModelVersion": "4.0"
}

11. Upload pictures on s3
$aws s3 cp wdm1.jpg s3://faces.walterdalmut.com/
$ aws s3 ls s3://faces.walterdalmut.com
2019-02-15 17:46:24 6753 thor1.jpg
2019-02-15 17:40:00 44897 wdm1.jpg

12. Detect faces (when you upload a picture)
$ aws rekognition detect-faces --image 'S3Object={Bucket=faces.walterdalmut.com,Name=wdm1.jpg}'
{
"FaceDetails": [
{
"Landmarks": [
{
"Type": "eyeLeft",
"X": 0.4940113127231598,
"Y": 0.28493359684944153
},
{
"Type": "eyeRight",
"X": 0.5633826851844788,
"Y": 0.2943187952041626
},
{
"Type": "mouthLeft",
"X": 0.4960087239742279,
"Y": 0.41778162121772766
},
{
"Type": "mouthRight",
"X": 0.5530909895896912,
"Y": 0.42552411556243896
},
{
"Type": "nose",
"X": 0.5306091904640198,
"Y": 0.34477418661117554
}
],
}
]
}
// continue...
{
"FaceDetails": [
{
"BoundingBox": {
"Width": 0.1618916094303131,
"Height": 0.4021730124950409,
"Left": 0.43909329175949097,
"Top": 0.12676428258419037
},
"Pose": {
"Roll": 3.8218839168548584,
"Yaw": 5.0268073081970215,
"Pitch": 5.38221549987793
},
"Quality": {
"Brightness": 64.7296142578125,
"Sharpness": 32.20803451538086
},
"Confidence": 99.9999771118164
}
]
}

13. Index faces (when you upload a picture)
$ aws rekognition index-faces
--collection-id collection-11
--image 'S3Object={Bucket=faces.walterdalmut.com,Name=wdm1.jpg}'
{
"FaceRecords": [
{
"Face": {
"FaceId": "310b383e-66c1-43b8-918a-75388619ee7b",
"BoundingBox": {
"Width": 0.1618916094303131,
"Height": 0.4021730124950409,
"Left": 0.43909329175949097,
"Top": 0.12676428258419037
},
"ImageId": "da1ae019-db88-3713-8e9e-7d01238ea609",
"Confidence": 99.9999771118164
},
}
]
…
}

14. Index faces (when you upload a picture)
$ aws rekognition index-faces
--collection-id collection-11
--image 'S3Object={Bucket=faces.walterdalmut.com,Name=wdm1.jpg}'
--external-image-id my-user-id
{
"FaceRecords": [
{
"Face": {
"FaceId": "my-user-id",
"BoundingBox": {
"Width": 0.1618916094303131,
"Height": 0.4021730124950409,
"Left": 0.43909329175949097,
"Top": 0.12676428258419037
},
"ImageId": "da1ae019-db88-3713-8e9e-7d01238ea609",
"Confidence": 99.9999771118164
},
}
]
…
}

15. Search faces (when you upload a picture)
$ aws rekognition search-faces-by-image
--collection-id collection-11
--image 'S3Object={Bucket=faces.walterdalmut.com,Name=wdm1.jpg}'
{
"SearchedFaceBoundingBox": {
"Width": 0.1618916094303131,
"Height": 0.4021730124950409,
"Left": 0.43909329175949097,
"Top": 0.12676428258419037
},
"SearchedFaceConfidence": 99.9999771118164,
"FaceMatches": [
{
"Similarity": 99.99999237060547,
"Face": {
"FaceId": "my-user-id",
"BoundingBox": {
"Width": 0.16189199686050415,
"Height": 0.4021730124950409,
"Left": 0.4390929937362671,
"Top": 0.12676399946212769
},
"ImageId": "da1ae019-db88-3713-8e9e-7d01238ea609",
"Confidence": 100.0
}
}
],
"FaceModelVersion": "4.0"
}

16. List existing faces (management)
$ aws rekognition list-faces --collection-id collection-16
{
"Faces": [
{
"FaceId": "18215496-3282-46ae-a265-aecf140fba65",
"BoundingBox": {
"Width": 0.37553301453590393,
"Height": 0.3338260054588318,
"Left": 0.43626898527145386,
"Top": 0.289465993642807
},
"ImageId": "ea5e78fc-3fdb-32d3-b053-5a22f95dbd55",
"ExternalImageId": "6639219",
"Confidence": 100.0
},
{
"FaceId": "2b85a18f-7570-4568-9fe0-b770ace00745",
"BoundingBox": {
"Width": 0.47415900230407715,
"Height": 0.3649649918079376,
"Left": 0.34846198558807373,
"Top": 0.28914299607276917
},
"ImageId": "c601ee26-224b-308a-a25c-ecd56ad0a400",
"ExternalImageId": "6639219",
"Confidence": 100.0
},
],
"FaceModelVersion": "4.0"
}

17. Drop existing faces (management)
$ aws rekognition delete-faces --collection-id collection-16
--face-ids 2b85a18f-7570-4568-9fe0-b770ace00745
{
"DeletedFaces": [
"2b85a18f-7570-4568-9fe0-b770ace00745"
]
}

18. Thanks for listening.
Torino 2018 - walter.dalmut@corley.it