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Amazon Rekognition
- 1. © 2016, Amazon Web Services, Inc. or its Affiliates. All rights reserved. David Pearson AWS AI Services March 2017 Amazon Rekognition search, verify, and organize millions of images
- 2. Amazon AI Intelligent Services Powered By Deep Learning
- 3. search, verify, and organize millions of images Amazon Rekognition Deep Learning-Based Image Recognition Service Object and Scene Detection Facial Analysis Face Comparison Facial Recognition
- 4. rich metadata index objects, scenes, facial attributes, persons
- 5. Rekognition APIs DetectLabels DetectFaces CompareFaces CreateCollection ListCollections DeleteCollection IndexFaces ListFaces DeleteFaces SearchFaces SearchFacesByImage Stateless (Non Storage) Collections (face storage) Faces (within collections) Search (within collections)
- 6. Thousands of Objects and Scenes • Search, filter, and curate image libraries • Photo, real estate, vacation rental, travel, hospitality, and more High Rise Coast City Pier Water Waterfront Dawn Outdoors Harbor Sky Building
- 7. Amazon Rekognition API { "Confidence": 94.62968444824219, "Name": "adventure" }, { "Confidence": 94.62968444824219, "Name": "boat" }, { "Confidence": 94.62968444824219, "Name": "rafting" }, . . . DetectLabels
- 8. 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
- 9. Use Case: Real Estate Property Search
- 10. Facial Analysis Locate faces within images and analyze face attributes to detect emotion, pose, facial landmarks, and features • Avoid faces when cropping images and overlaying ads • Capture user demographics and sentiment • Recommend the best photos • Improve online dating match recommendations • Dynamic, personalized ads
- 11. Amazon Rekognition API [ { "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
- 12. Face Location avoid cropping and ad overlays that cover faces { "Attributes": [ "string" ], "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } } } Request "FaceDetail": [ ... { "BoundingBox": { "Height": number, "Left": number, "Top": number, "Width": number }, "Confidence": number, ... Response (excerpt)
- 13. Sentiment Analysis passive capture of in-store customer sentiment "FaceDetail": [ ... { "Emotions": [ { "Confidence": number, "Type": "string" } ... Response (excerpt) "Confidence": 93.29251861572266, "Type": "HAPPY" "Confidence": 28.57428741455078, "Type": "CALM" "Confidence": 1.4989674091339111, "Type": "ANGRY"
- 14. Feature Location add face overlays in social media apps "FaceDetail": [ ... { "Landmarks": [ { "Type": "string", "X": number, "Y": number } ... Response (excerpt)EyeLeft EyeRight Nose MouthLeft MouthRight LeftPupil RightPupil LeftEyeBrowLeft LeftEyeBrowRight LeftEyeBrowUp :
- 15. Demographic Analysis measure the impact of targeted marketing campaigns "FaceDetail": [ ... { "AgeRange": { "High": number, "Low": number },... "Gender": { "Confidence": number, "Value": Boolean } ... Response (excerpt) Look Your Best All Day Time for A New Look? PersonAPersonB Sees Sees
- 16. Use Case: Retail Store Demographic Analysis
- 17. Image Quality and Direction of Gaze detect blurry or poor quality light; subject liveness "FaceDetail": [ ... { "Pose": { "Pitch": number, "Roll": number, "Yaw": number }, "Quality": { "Brightness": number, "Sharpness": number } Response (excerpt) "Pose": { "Pitch": 8.250975608825684, "Roll": -8.29802131652832, "Yaw": 14.244261741638184 }, "Quality": { "Brightness": 46.14684295654297, "Sharpness": 99.9945297241211 },
- 18. Face Comparison Measure the likelihood that faces in two images are of the same person • Add face verification to applications and devices • Extend physical security controls • Provide guest access to VIP-only facilities • Verify users for online exams and polls
- 19. Amazon Rekognition API { "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
- 20. Use Case: Employee Badge Scan
- 21. Facial Recognition Identify people in images by finding the closest match for an input face image against a collection of stored face vectors • Add friend tagging to social and messaging apps • Assist public safety officers find missing persons • Identify employees as they access sensitive locations • Recognize celebrities in historical image archives
- 22. Amazon Rekognition API Facial Recognition index and search faces in a collection Index Search Collection IndexFaces SearchFacesByImage
- 23. Amazon Rekognition API f7a3a278-2a59-5102-a549-a12ab1a8cae8 & vector001 02e56305-1579-5b39-ba57-9afb0fd8782d & vector002 Face ID & vector<float>Face 4c55926e-69b3-5c80-8c9b-78ea01d30690 & vector003transformed stored { f7a3a278-2a59-5102-a549-a12ab1a8cae8, 02e56305-1579-5b39-ba57-9afb0fd8782d, 4c55926e-69b3-5c80-8c9b-78ea01d30690 } IndexFaces Collection
- 24. Amazon Rekognition API Face SearchFacesbyImage Collection Nearest neighbor search FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690 Similarity: 97 FaceID: 92b8731e-f490-77fe-3af7-c6683ba8f533 Similarity: 92 FaceID: 73d908ff-a544-1c78-9da2-7fcc185aae91 Similarity: 85
- 25. Use Case: Find Images of Friends
- 26. Lambda Blueprint for Rekognition
- 27. AMAZON S3 AWS LAMBDA AMAZON REKOGNITION AMAZON DYNAMODB Triggered Media Indexing When a new image is uploaded to S3… 1. Lambda function is triggered and calls Rekognition 2. Rekognition API retrieves the image from S3 and returns the detected labels, attributes, and recognized persons 3. The output metadata is stored into DynamoDB for durability and speed of access
- 28. Fast Analysis of New Image Files in S3 Console Demo Event-driven processing with Lambda • Add new images to an S3 bucket • Lambda log as it processes new images • View metadata in DynamoDB table
- 29. Detect Faces Index Faces Search Faces Batch Processing a Media Archive
- 30. Influencer Marketing Case Study Associate influencers with objects and scenes in social media images in order to create high impact campaigns for clients Using 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
- 31. Law Enforcement Case Study To service leads from citizens and security cameras, a person spends days manually searching thousands of images The new Rekognition-powered mobile and web app compares field images with photos of previous offenders: • Helps identify unknown theft suspects from security footage • Provides leads by identifying possible witnesses and accomplices • Identifies people of interest who do not have identification
- 32. Media Case Study Identify who is on camera at what time for each of 8 networks so that recorded video streams 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 S3 and call Rekognition to find best facial match • Store time stamp and faceID metadata
- 33. C-SPAN Architecture Video feeds encoded from 8 locations (3 networks and 5 federal courthouses) Frames extracted into JPGs and hosted in S3 SQS provides asynchronous decoupling Search Rekognition collection for high similarity matches Results cache drives search and discovery requests R3 hashing detects if a scene significantly changes
- 34. Rekognition Customers Digital Asset Management Media and Entertainment Influencer Marketing 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
Hinweis der Redaktion
- Introducing Amazon Rekognition - a fully managed deep learning based image recognition service. Rekognition was designed from the get-go to run at scale. It comprehends scenes, objects, concepts and faces. Given an image, it will return a list of labels. Given an image with one or more faces,it will return bounding boxes for each face, along with face attributes. Given two images with faces, it will compare the largest face from the source image and find similarity with faces found in the tagret image. Rekognition provides quality face recognition at scale, and supports creation of collection of millions of faces and search of similar faces in the collection. Now lets dive into each of these features and look at the API that support these features.
- In its simplest form – DetectLabels takes an image as input and returns a set of labels with confidence score. Request { "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } }, "MaxLabels": number, "MinConfidence": number } Response { "Labels": [ { "Confidence": number, "Name": "string" } ], "OrientationCorrection": "string" } In the preceding example, the operation returns one label for each of the three objects. The operation can also return multiple labels for the same object in the image. For example, if the input image shows a flower (for example, a tulip), the operation might return the following three labels.
- A mobile app uploads new images to S3 An S3 notification triggers a Lambda function which calls Rekognition’s DetectLabels API with an S3 url DetectLabels analyzes the image and returns labels for objects and scenes detected in the image This output is persisted as metadata into DynamoDB to ensure durability and into Elasticsearch to power a smart search index. The application serves selected images directly from S3. Options on this configuration include: writing to Elasticsearch directly from the Lambda function, and using CloudFront to serve the image to the user.
- DetectFaces taken an image with one or more faces and returns bounding box of the faces and some key landmarks and attributes for each face detected. Input { "Attributes": [ "string" ], "Image": { "Bytes": blob, "S3Object": { "Bucket": "string", "Name": "string", "Version": "string" } } } Talk about the attributes.
- A mobile app uploads new images to S3 An S3 notification triggers a Lambda function which calls Rekognition’s DetectLabels API with an S3 url DetectLabels analyzes the image and returns labels for objects and scenes detected in the image This output is persisted as metadata into DynamoDB to ensure durability and into Elasticsearch to power a smart search index. The application serves selected images directly from S3. Options on this configuration include: writing to Elasticsearch directly from the Lambda function, and using CloudFront to serve the image to the user.
- A mobile app uploads new images to S3 An S3 notification triggers a Lambda function which calls Rekognition’s DetectLabels API with an S3 url DetectLabels analyzes the image and returns labels for objects and scenes detected in the image This output is persisted as metadata into DynamoDB to ensure durability and into Elasticsearch to power a smart search index. The application serves selected images directly from S3. Options on this configuration include: writing to Elasticsearch directly from the Lambda function, and using CloudFront to serve the image to the user.
- A mobile app uploads new images to S3 An S3 notification triggers a Lambda function which calls Rekognition’s DetectLabels API with an S3 url DetectLabels analyzes the image and returns labels for objects and scenes detected in the image This output is persisted as metadata into DynamoDB to ensure durability and into Elasticsearch to power a smart search index. The application serves selected images directly from S3. Options on this configuration include: writing to Elasticsearch directly from the Lambda function, and using CloudFront to serve the image to the user.
- The Amazon S3 inventory tool produces a .csv file, that lists the images that are stored in an images bucket, and saves the csv file to an S3 bucket called the inventory bucket. The inventory file is produced daily by the S3 service. When a new version of the zipped S3 inventory .csv file is saved to the destination S3 inventory bucket, a Lambda function is called as the inventory bucket is configured to invoke the Lambda function on any object with a .csv.gz file extension. The Lambda function reads the contents of the .csv file, and for each image it finds, it creates a new AWS Batch Job with the image bucket and name, and submits it to the AWS Batch queue. AWS Batch processes the batch jobs submitted to the job queue by launching EC2 instances and executing the batch jobs on those instances. The Lambda function removes the S3 event trigger to avoid the AWS Batch backfill workflow to run more than once. The AWS Batch jobs receive an image bucket and name as input parameters and check if the image has already been processed by querying the Amazon ES domain index. If the image has not been processed, the AWS Batch Job calls the Amazon Rekognition detect_label API. The AWS Batch jobs save the labels that Rekognition returns for the image into the Amazon ES domain index.
- Clients can request influencers in a key demographic. Rekognition partners use their metadata to identify high quality influencers for targeted campaigns, which may involve paying influencers for product use and social media posts featuring the product. An influencer’s strength is measured by who is following them on social media Future use for Reko: identify brands, measure impact, rate influencers
- Quoted Interesting Facts More than 300,000 photos of previous offenders indexed in 2 days Each search requires seconds to return results Entire process went from 2-3 days, to minutes Within 1 week of going live, the app was used to identify and arrest a suspect who stole over $5,000 from local stores. There were no other leads prior to the app finding the match. No other tool could scale to this volume. Plans to expand to neighboring counties, and beyond.
- Democratizing Image Analysis (key factors: affordable, accessible, scalable) that helps you get started in minutes