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Javier Cristancho – PSA AWS
Marzo - 2018
Amazon Rekognition
Deep learning-based image recognition service
Images – Universal, Ubiquitous, & Essential
There are 3,700,000,000 internet users in 2017
1,200,000,000 photos will be taken in 2017 (9% YoY Growth)
Source: InfoTrends Worldwide
Amazon Rekognition
Extract rich metadata from visual content
Object and Scene
Detection
Activity Detection
Facial
Analysis
Face
Comparison
Facial
Recognition
Person Tracking
Real time live Stream
Celebrity
Recognition
Image
Moderation
Why use Rekognition?
johnf
Object & Scene Detection
Object and scene detection makes it easy for you to add features that search,
filter, and curate large image libraries.
Identify objects and scenes and provide confidence scores
DetectLabels
Flower
Arrangement
Chair
Coffee Table
Living Room Indoors
Furniture
Cushion
Vase
Maple
Villa
Plant
Garden
Water
Swimming Pool
Tree
Potted Plant
Backyard
Patio
Facial Analysis
Analyze facial characteristics in multiple dimensions
DetectFaces
Image Quality
Facial Landmarks
Demographic Data Emotion Expressed
General Attributes
Facial Pose
Brightness 23.6%
Sharpness 99.9%
EyeLeft,EyeRight,Nose
RightPupil,LeftPupil
MouthRight,LeftEyeBrowUp
Bounding Box...
Age Range 29-45
Gender:Male 96.5%
Happy 83.8%
Surprised 0.65%
Smile:True 23.6%
EyesOpen:True 99.8%
Beard:True 99.5%
Mustache:True 99.9%...
Pitch 1.446
Roll 5.725
Yaw 4.383
Face Comparison
Measure the likelihood that faces are of the same person
Similarity 93% Similarity 0%
CompareFaces
Facial Recognition
Find similar faces in a large collection of images
SearchFacesByImage
Search
Index
Collection
Celebrity Recognition & Image Moderation
Newly released Rekognition features
Detect explicit and suggestive contentRecognize thousands of famous individuals
DetectModerationLabelsRecognizeCelebrities
https://console.aws.amazon.com/rekognition/home
What Can You Do with Amazon Rekognition?
• Search for people, objects, scenes, and concepts
across millions of images
• Filter inappropriate or specific content
• Verify identities by matching against reference faces
• Recognize individuals by matching faces to a collection
• Analyze user traffic hotspots and journey paths by
demographics and sentiment
Sentiment Analysis - Use Case
(Retail – In-store and Online)
Demographic and Sentiment Analysis
Female
Happy
Smiling
Male
No Facial Hair
Happy
Female
Sad
No Eyeglasses
Sentiment Analysis
Amazon RedshiftAmazon Quicksight
Live Subject In-Store Camera Application
Amazon S3
Analyze Faces
Shoppers enter and
browse in retail store
In-store cameras capture
live images of shoppers
A Lambda function is triggered
and calls Rekognition Rekognition analyzes the image and returns
facial attributes detected, which include
emotion and demographic detail
Return data is normalized and
staged in S3 en route to Redshift
Marketing Reports
Periodic ingest of data into
Redshift
Regular analysis to identify trends in
demographic activity and in-store
sentiment over time
Trend reporting for retail store locations
Look Your Best All Day
Time for A New Look?
Facial Analysis - Use Case
(Targeted Marketing)
Demographic and Sentiment Analysis
PersonAPersonB
Sees
Sees
Facial Analysis - Use Case
(Targeted Marketing)
Demographic and Sentiment Analysis
demographic and
sentiment attributes
Look Your Best All Day
display ad image
Application
AMAZON
REDSHIFT
AMAZON
DYNAMODBAMAZON S3
log
demographic
profile updates
retrieve ad image
face image is collected
and analyzed AMAZON
REKOGNITION
DetectFaces
Store
M
etadata
Collections and Access Patterns
Logging - public events; visitor logs; digital libraries
• One large collection per event/time period
• Wide searches
Social Tagging - photo storage and sharing
• One collection per application user
• Automated friend tagging
Person Verification - employee gate check
• One collection for each person to be verified
• Detection of stolen/shared IDs
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
Rekognition APIs – Overview
Rekognition’s computer vision API operations can be grouped into
Non-storage API operations, and Storage-based API operations
CompareFaces
DetectFaces
DetectLabels
DetectModerationLabels
GetCelebrityInfo
RecognizeCelebrities
Non-storage API Operations
CreateCollection
DeleteCollection
DeleteFaces
IndexFaces
ListCollections
SearchFaces
SearchFacesByImage
Storage-based API Operations
ListFaces
Rekognition APIs – Advanced Usage
Decision trees and processing pipelines
Why?
• Many use cases require more than a single
operation to arrive at actionable data
How?
• S3 event notifications, Lambda, Step Functions
• DynamoDB for persistent pipeline storage
• Augmenting results with 3rd Party AI/ML
• OpenCV, MXNet, etc. on EC2 Spot, ECS, AI/ML AMI
Sample Use Cases
• Person of interest near a celebrity
• Multi-pass motion detection enhancement
• Subjects leaving a location without possessions
IndexFaces
DetectLabels
“person”
Rekognition APIs – Advanced Usage
Person of Interest Near a Celebrity
aws rekognition recognize-celebrities –image “S3Object={Bucket=mybucket,Name=cam.jpg}”
aws rekognition search-faces-by-image –image “S3Object={Bucket=mybucket,Name=cam.jpg}” 
--collection-id “persons-of-interest"
aws rekognition create-collection --collection-id “persons-of-interest”
aws rekognition index-faces --image “S3Object={Bucket=mybucket,Name=subject.jpg}” 
--collection-id “persons-of-interest”
Rekognition APIs – Advanced Usage
Person of Interest Near a Celebrity
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
CompareFaces
DetectFaces
DetectLabels
DetectModerationLabels
GetCelebrityInfo
RecognizeCelebrities
2
{
"FaceMatches": [
{"Face": {"BoundingB
"Height":
0.2683333456516266,
"Left":
0.5099999904632568,
"Top":
0.1783333271741867,
"Width":
0.17888888716697693},
"
CreateCollection
DeleteCollection
DeleteFaces
IndexFaces
ListCollections
SearchFaces
SearchFacesByImage
ListFaces
3
1
• Built in 3 weeks
• Indexed against 99,000 people
• Index created in one day
• Saved ~9,000 hours a year in
manual curation costs
• Live video with frame sampling
Automating Footage Tagging with
Amazon Rekognition
Previously, only about half of all footage was indexed due to the
immense time requirements required by manual processes
Automating Footage Tagging with
Amazon Rekognition
Solution Architecture
EncodersStills
Extraction &
FeedsResults
Cache
Bucket
R3
Amazon
Rekognition
users
Stills
Frames
SQS
Trigger
1
2
3
4
Quickly Identifying Persons of Interest
with Amazon Rekognition
• More than 300,000 mugshots indexed within 1-2 days
• Identification process went from days (and weeks), to seconds
– greatly increasing the ability for law enforcement to act quickly
• Within 1 week of going live, the application helped identify a
suspect in a case that had no leads, leading to an arrest
There was no software on the market that allows users to quickly search
hundreds of thousands of images using a face in another image
Quickly Identifying Persons of Interest
with Amazon Rekognition
Solution Architecture
MS SQL
Database
ColdFusion
Web Service
Mugshot
Bucket
T2.Micro
MySQL DB
instance
Amazon
Rekognition
Amazon
Cognito
iOSMobile
Client
client PHP
1
2
3
4
• Media and Entertainment
• Digital Asset Management
• Safety and Security
• Law Enforcement
• Consumer Electronics
• Influencer Marketing
• Consumer Storage
• Travel and Hospitality
• Digital Advertising
• eCommerce
• Education
Rekognition Customers
Rekognition - Summary
• Leverage Amazon internal experience with
AI, ML and Computer Vision
• Managed API service with embedded AI for
maximum accessibility and simplicity
• Integrates natively with other AWS Services
• Extensible by design
LETS GET
HANDS DIRTY !
Face-Based User Verification
Authenticated User
Image Capture Application
Amazon S3
Compare Faces
If the similarity score is over 92%, the
application returns a green status. If not,
an alert is issued to security staff.
The application captures a live
image of each employee as
they scan their access card
Rekognition compares the live image
and the badge image – and returns
a similarity score
The application retrieves the
user’s badge from S3
Confirm user identities by comparing their live image with a reference image
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
GRACIAS

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Globant - Amazon recognition workshop - 2018

  • 1. Javier Cristancho – PSA AWS Marzo - 2018 Amazon Rekognition Deep learning-based image recognition service
  • 2. Images – Universal, Ubiquitous, & Essential There are 3,700,000,000 internet users in 2017 1,200,000,000 photos will be taken in 2017 (9% YoY Growth) Source: InfoTrends Worldwide
  • 3. Amazon Rekognition Extract rich metadata from visual content Object and Scene Detection Activity Detection Facial Analysis Face Comparison Facial Recognition Person Tracking Real time live Stream Celebrity Recognition Image Moderation
  • 5. Object & Scene Detection Object and scene detection makes it easy for you to add features that search, filter, and curate large image libraries. Identify objects and scenes and provide confidence scores DetectLabels Flower Arrangement Chair Coffee Table Living Room Indoors Furniture Cushion Vase Maple Villa Plant Garden Water Swimming Pool Tree Potted Plant Backyard Patio
  • 6. Facial Analysis Analyze facial characteristics in multiple dimensions DetectFaces Image Quality Facial Landmarks Demographic Data Emotion Expressed General Attributes Facial Pose Brightness 23.6% Sharpness 99.9% EyeLeft,EyeRight,Nose RightPupil,LeftPupil MouthRight,LeftEyeBrowUp Bounding Box... Age Range 29-45 Gender:Male 96.5% Happy 83.8% Surprised 0.65% Smile:True 23.6% EyesOpen:True 99.8% Beard:True 99.5% Mustache:True 99.9%... Pitch 1.446 Roll 5.725 Yaw 4.383
  • 7. Face Comparison Measure the likelihood that faces are of the same person Similarity 93% Similarity 0% CompareFaces
  • 8. Facial Recognition Find similar faces in a large collection of images SearchFacesByImage Search Index Collection
  • 9. Celebrity Recognition & Image Moderation Newly released Rekognition features Detect explicit and suggestive contentRecognize thousands of famous individuals DetectModerationLabelsRecognizeCelebrities
  • 11. What Can You Do with Amazon Rekognition? • Search for people, objects, scenes, and concepts across millions of images • Filter inappropriate or specific content • Verify identities by matching against reference faces • Recognize individuals by matching faces to a collection • Analyze user traffic hotspots and journey paths by demographics and sentiment
  • 12. Sentiment Analysis - Use Case (Retail – In-store and Online) Demographic and Sentiment Analysis Female Happy Smiling Male No Facial Hair Happy Female Sad No Eyeglasses
  • 13. Sentiment Analysis Amazon RedshiftAmazon Quicksight Live Subject In-Store Camera Application Amazon S3 Analyze Faces Shoppers enter and browse in retail store In-store cameras capture live images of shoppers A Lambda function is triggered and calls Rekognition Rekognition analyzes the image and returns facial attributes detected, which include emotion and demographic detail Return data is normalized and staged in S3 en route to Redshift Marketing Reports Periodic ingest of data into Redshift Regular analysis to identify trends in demographic activity and in-store sentiment over time Trend reporting for retail store locations
  • 14. Look Your Best All Day Time for A New Look? Facial Analysis - Use Case (Targeted Marketing) Demographic and Sentiment Analysis PersonAPersonB Sees Sees
  • 15. Facial Analysis - Use Case (Targeted Marketing) Demographic and Sentiment Analysis demographic and sentiment attributes Look Your Best All Day display ad image Application AMAZON REDSHIFT AMAZON DYNAMODBAMAZON S3 log demographic profile updates retrieve ad image face image is collected and analyzed AMAZON REKOGNITION DetectFaces Store M etadata
  • 16. Collections and Access Patterns Logging - public events; visitor logs; digital libraries • One large collection per event/time period • Wide searches Social Tagging - photo storage and sharing • One collection per application user • Automated friend tagging Person Verification - employee gate check • One collection for each person to be verified • Detection of stolen/shared IDs
  • 17. { "FaceMatches": [ {"Face": {"BoundingB "Height": 0.2683333456516266, "Left": 0.5099999904632568, "Top": 0.1783333271741867, "Width": 0.17888888716697693}, " { "FaceMatches": [ {"Face": {"BoundingB "Height": 0.2683333456516266, "Left": 0.5099999904632568, "Top": 0.1783333271741867, "Width": 0.17888888716697693}, " Rekognition APIs – Overview Rekognition’s computer vision API operations can be grouped into Non-storage API operations, and Storage-based API operations CompareFaces DetectFaces DetectLabels DetectModerationLabels GetCelebrityInfo RecognizeCelebrities Non-storage API Operations CreateCollection DeleteCollection DeleteFaces IndexFaces ListCollections SearchFaces SearchFacesByImage Storage-based API Operations ListFaces
  • 18. Rekognition APIs – Advanced Usage Decision trees and processing pipelines Why? • Many use cases require more than a single operation to arrive at actionable data How? • S3 event notifications, Lambda, Step Functions • DynamoDB for persistent pipeline storage • Augmenting results with 3rd Party AI/ML • OpenCV, MXNet, etc. on EC2 Spot, ECS, AI/ML AMI Sample Use Cases • Person of interest near a celebrity • Multi-pass motion detection enhancement • Subjects leaving a location without possessions IndexFaces DetectLabels “person”
  • 19. Rekognition APIs – Advanced Usage Person of Interest Near a Celebrity
  • 20. aws rekognition recognize-celebrities –image “S3Object={Bucket=mybucket,Name=cam.jpg}” aws rekognition search-faces-by-image –image “S3Object={Bucket=mybucket,Name=cam.jpg}” --collection-id “persons-of-interest" aws rekognition create-collection --collection-id “persons-of-interest” aws rekognition index-faces --image “S3Object={Bucket=mybucket,Name=subject.jpg}” --collection-id “persons-of-interest” Rekognition APIs – Advanced Usage Person of Interest Near a Celebrity { "FaceMatches": [ {"Face": {"BoundingB "Height": 0.2683333456516266, "Left": 0.5099999904632568, "Top": 0.1783333271741867, "Width": 0.17888888716697693}, " CompareFaces DetectFaces DetectLabels DetectModerationLabels GetCelebrityInfo RecognizeCelebrities 2 { "FaceMatches": [ {"Face": {"BoundingB "Height": 0.2683333456516266, "Left": 0.5099999904632568, "Top": 0.1783333271741867, "Width": 0.17888888716697693}, " CreateCollection DeleteCollection DeleteFaces IndexFaces ListCollections SearchFaces SearchFacesByImage ListFaces 3 1
  • 21. • Built in 3 weeks • Indexed against 99,000 people • Index created in one day • Saved ~9,000 hours a year in manual curation costs • Live video with frame sampling Automating Footage Tagging with Amazon Rekognition Previously, only about half of all footage was indexed due to the immense time requirements required by manual processes
  • 22. Automating Footage Tagging with Amazon Rekognition Solution Architecture EncodersStills Extraction & FeedsResults Cache Bucket R3 Amazon Rekognition users Stills Frames SQS Trigger 1 2 3 4
  • 23. Quickly Identifying Persons of Interest with Amazon Rekognition • More than 300,000 mugshots indexed within 1-2 days • Identification process went from days (and weeks), to seconds – greatly increasing the ability for law enforcement to act quickly • Within 1 week of going live, the application helped identify a suspect in a case that had no leads, leading to an arrest There was no software on the market that allows users to quickly search hundreds of thousands of images using a face in another image
  • 24. Quickly Identifying Persons of Interest with Amazon Rekognition Solution Architecture MS SQL Database ColdFusion Web Service Mugshot Bucket T2.Micro MySQL DB instance Amazon Rekognition Amazon Cognito iOSMobile Client client PHP 1 2 3 4
  • 25. • Media and Entertainment • Digital Asset Management • Safety and Security • Law Enforcement • Consumer Electronics • Influencer Marketing • Consumer Storage • Travel and Hospitality • Digital Advertising • eCommerce • Education Rekognition Customers
  • 26. Rekognition - Summary • Leverage Amazon internal experience with AI, ML and Computer Vision • Managed API service with embedded AI for maximum accessibility and simplicity • Integrates natively with other AWS Services • Extensible by design
  • 28. Face-Based User Verification Authenticated User Image Capture Application Amazon S3 Compare Faces If the similarity score is over 92%, the application returns a green status. If not, an alert is issued to security staff. The application captures a live image of each employee as they scan their access card Rekognition compares the live image and the badge image – and returns a similarity score The application retrieves the user’s badge from S3 Confirm user identities by comparing their live image with a reference image
  • 29. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. GRACIAS