This session will introduce you to Amazon Rekognition, a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API lets you easily build powerful visual search and discovery into your applications. With Amazon Rekognition, you only pay for the images you analyze and the face metadata you store. There are no minimum fees and there are no upfront commitments. To get started with Rekognition, simply log in to the Rekognition console to try the service with sample photos or your own photos.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
David Pearson, AWS AI Services
May 2017
Amazon Rekognition
Extract Rich Image Metadata from Visual Content

2. Amazon AI
Intelligent Services Powered By Deep Learning

3. Rich Metadata Index
objects, scenes, facial attributes, persons
Amazon Rekognition
Deep Learning-Based Image Recognition Service

4. Deer 98.8%
Wildlife 95.1%
Conifer 95.1%
Spruce 95.1%
Wood 78.3%
Tree 63.5%
Forest 63.5%
Vegetation 61.9%
Pine 60.6%
Outdoors 54.0%
Flower 53.9%
Plant 52.9%
Nature 50.7%
Field 50.7%
Grass 50.7%

5. {
"Image": {
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
},
"MaxLabels": number,
"MinConfidence": number
}
DetectLabels
Amazon S3
Image Bucket

6. DetectLabels
"Labels": [
{
"Confidence": 98.9294204711914,
"Name": "Moss"
},
{
"Confidence": 98.9294204711914,
"Name": "Plant"
},
{
"Confidence": 97.35887908935547,
"Name": "Creek"
},
{
"Confidence": 97.35887908935547,
"Name": "Outdoors"
},
{
"Confidence": 97.35887908935547,
"Name": "Stream"
},
{
"Confidence": 97.35887908935547,
"Name": "Water"
},

7. DetectLabels
Generate labels for objects, scenes, and concepts in input images
{
"Image": {
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
},
"MaxLabels": number,
"MinConfidence": number
}
Request
{
"Labels": [
{
"Confidence": number,
"Name": "string"
}
],
"OrientationCorrection":
"string"
}
Response

8. Age Range 38-59
Beard: False 84.3%
Emotion: Happy 86.5%
Eyeglasses: False 99.6%
Eyes Open: True 99.9%
Gender: Male 99.9%
Mouth Open: False 86.2%
Mustache: False 98.4%
Smile: True 95.9%
Sunglasses: False 99.8%
Bounding Box
Height: 0.36716..
Left: 0.40222..
Top: 0.23582..
Width: 0.27222..
Landmarks
EyeLeft
EyeRight
Nose
MouthLeft
MouthRight
LeftPupil
RightPupil
LeftEyeBrowLeft
LeftEyeBrowRight
LeftEyeBrowUp
:
Quality
Brightness 52.5%
Sharpness 99.9%

9. "BoundingBox": {
"Height": 0.3449999988079071,
"Left": 0.09666666388511658,
"Top": 0.27166667580604553,
"Width": 0.23000000417232513
},
"Confidence": 100,
"Emotions": [
{"Confidence": 99.1335220336914,
"Type": "HAPPY" },
{"Confidence": 3.3275485038757324,
"Type": "CALM"},
{"Confidence": 0.31517744064331055,
"Type": "SAD"}
],
"Eyeglasses": {"Confidence": 99.8050537109375,
"Value": false},
"EyesOpen": {Confidence": 99.99979400634766,
"Value": true},
"Gender": {"Confidence": 100,
"Value": "Female”}
DetectFaces
smart cropping
& ad overlays
sentiment
capture
demographic
analysis
face editing
& pixelation

10. Similarity 93%
Similarity 0%

11. "FaceMatches": [
{"Face": {"BoundingBox": {
"Height": 0.2683333456516266,
"Left": 0.5099999904632568,
"Top": 0.1783333271741867,
"Width": 0.17888888716697693},
"Confidence": 99.99845123291016},
"Similarity": 96
},
{"Face": {"BoundingBox": {
"Height": 0.2383333295583725,
"Left": 0.6233333349227905,
"Top": 0.3016666769981384,
"Width": 0.15888889133930206},
"Confidence": 99.71249389648438},
"Similarity": 0
}
],
"SourceImageFace": {"BoundingBox": {
"Height": 0.23983436822891235,
"Left": 0.28333333134651184,
"Top": 0.351423978805542,
"Width": 0.1599999964237213},
"Confidence": 99.99344635009766}
}
CompareFaces

12. Image Moderation
Detect images with explicit or suggestive adult content
Optimize manual review processes

13. Image Moderation
Hierarchical taxonomy
Confidence score
"ModerationLabels": [
{
"Confidence": 82.7555923461914,
"Name": "Suggestive",
"ParentName": ""
},
{
"Confidence": 82.7555923461914,
"Name": "Female Swimwear or Underwear",
"ParentName": "Suggestive"
},
{
"Confidence": 50.11056137084961,
"Name": "Nudity and Sexuality",
"ParentName": ""

14. DetectModerationLabels API
Top-Level Category Child Category
Explicit Nudity Nudity
Graphic Male Nudity
Graphic Female Nudity
Sexual Activity
Partial Nudity
Suggestive Female Swimwear Or Underwear
Male Swimwear Or Underwear
Revealing Clothes
{
"Image": {
"Bytes": blob,
"S3Object": {
"Bucket": "string",
"Name": "string",
"Version": "string"
}
},
"MinConfidence": number
}
Request Syntax

15. Amazon Rekognition Console
https://console.aws.amazon.com/rekognition/home

17. Collection
IndexFaces
SearchFacesbyImage
Nearest neighbor
search
FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690
Similarity: 97
FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d
Similarity: 92
FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d
Similarity: 85

18. Collections and Access Patterns
Logging (public events; daily visitor logs; digital libraries)
• One potentially large collection per event / time period
• Enables wide searches
Social Tagging (photo storage and sharing)
• One collection per application user
• Enables automated friend tagging
Person Verification (employee gate check)
• One collection for each person to be verified
• Enables detection of stolen/shared IDs

19. Collection and Access Patterns
# Collections
# Faces per Collection
Person
Verification
Social Friend
Tagging
Event Logging /
Wide Search
1M

20. Amazon Rekognition Customers
• Law Enforcement and Public Safety
• Travel and Hospitality
• Digital Marketing and Advertising
• Media and Entertainment
• Internet of Things (IoT)

21. Law Enforcement and Public Safety
Washington County Sheriff (OR)
To follow leads from citizens & security cameras, a person
spends days manually searching thousands of images
The mobile and web app powered by Amazon Rekognition
compares new images with photos of previous offenders:
• Helps identify unknown theft suspects from security footage
• Provides leads by identifying possible witnesses & accomplices
• Identifies persons of interest who do not have identification

22. Travel and Hospitality
Anticipatory guest experiences for hotels using Amazon
Rekognition for facial recognition and sentiment capture
Kaliber is using Amazon Rekognition to help front desk agents
enhance relationships with guests:
• Recognize guests early for instant and personalized service
• Receive rich, contextualized guest information in real time
• Track guest sentiment throughout their stay
• Drive an 80% increase in guest satisfaction scores

23. Guest Workflow
Walk in Be recognized Be greeted
Capture sentiment to
trigger actionsEnjoy personalized serviceLeave with a fond farewell
“Kaliber allows us to bond with our guests from the
second they walk in my hotel.” – GM of a 5-star property

24. Influencer Marketing
Associate influencers with objects and scenes in social media
images in order to create high impact campaigns for clients
Using Amazon Rekognition for metadata extraction:
• Create rich media indexes of images from social media feeds, which
the application associates with influencers
• Enable analytics to profile environments where influence is strongest
• Connect client brands with the influencers most likely to have impact

25. Media and Entertainment
Identify who is on camera for each of 8 networks so
that recorded video can be indexed and searched
Video frame-sampling facial recognition solution
using Amazon Rekognition:
• Indexed 97,000 people into a face collection in 1 day
• Sample frames every 6 secs and test for image variance
• Upload images to Amazon S3 and call Amazon Rekognition
to find best facial match
• Store time stamp and faceID metadata

26. C-SPAN Indexing Architecture
Video feeds encoded from
8 locations (3 networks and
5 federal courthouses)
Frames extracted into
JPGs and hosted in
Amazon S3
Amazon SQS provides
asynchronous decoupling
Search Amazon Rekognition
collection for high similarity
matches
Results cache
drives search and
discovery requests
R3 hashing detects if a
scene significantly changes

30. Rekognition Demo with Video Frame Sampling

31. IoT Use Case
real-time facial recognition at the edge
AWS Advanced Consulting Partner
• Migrations
• DevOps
• Managed Services
• Software & Hardware Engineering
• User Experience & Visual Design
• Rapid Prototyping
AWS Competencies: DevOps, IoT, Healthcare

32. NERF CS-18 N-Strike Elite Rapidstrike
Adafruit 2.8”
PiTFT display
Raspberry Pi 3
Amazon Rekognition
https://sturdy.cloud/sting/
Training
Image

34. Amazon Rekognition
Customers
• Digital Asset Management
• Media and Entertainment
• Travel and Hospitality
• Influencer Marketing
• Systems Integration
• Digital Advertising
• Consumer Storage
• Law Enforcement
• Public Safety
• eCommerce
• Education

35. Amazon Rekognition Availability and Pricing
Free Tier: 5000 images processed per month for first 12 months
General Availability in 3 regions:
US East (N. Virginia), US West (Oregon); EU (Ireland)
Image Analysis Tiers Price per 1000
images processed
First 1 million images processed* per month $1.00
Next 9 million images processed* per month $0.80
Next 90 million images processed* per month $0.60
Over 100 million images processed* per month $0.40

36. Developer Resources and more…
https://aws.amazon.com/blogs/ai/
https://aws.amazon.com/rekognition

37. Thank You!
pearsond@amazon.com