Tag: HAQM Rekognition

We have seen a lot of discussion this past week about the role of HAQM Rekognition in facial recognition, surveillance, and civil liberties, and we wanted to share some thoughts.

HAQM Rekognition is a service we announced in 2016. It makes use of new technologies – such as deep learning – and puts them in the hands of developers in an easy-to-use, low-cost way. Since then, we have seen customers use the image and video analysis capabilities of HAQM Rekognition in ways that materially benefit both society (e.g. preventing human trafficking, inhibiting child exploitation, reuniting missing children with their families, and building educational apps for children), and organizations (enhancing security through multi-factor authentication, finding images more easily, or preventing package theft). HAQM Web Services (AWS) is not the only provider of services like these, and we remain excited about how image and video analysis can be a driver for good in the world, including in the public sector and law enforcement.

In this blog post, we’ll use your webcam on your laptop to send a live feed to an HAQM Kinesis Video Stream. From there, a processor within HAQM Rekognition Video analyzes the feed and compares it to a collection we create. The output matches will get sent to us via an email through an integration with AWS Lambda and HAQM Simple Notification Service (HAQM SNS).

Whatever your use case, real-time face recognition with Kinesis Video Streams and Rekognition Video is easy to set up and doesn’t require expensive hardware. The entire system built here is serverless and Rekognition Video qualifies for the AWS Free Tier.

In this blog post, you will learn how to combine the capabilities of HAQM Rekognition Video and HAQM Elastic Transcoder to automatically convert a long video into a highlight video showing all footage of a given person.

HAQM Rekognition today announces three new features: detection and recognition of text in images, real-time face recognition across tens of millions of faces, and detection of up to 100 faces in challenging crowded photos. Customers who are already using HAQM Rekognition for face verification and identification will experience up to a 10% accuracy improvement in most cases.

HAQM Rekognition is an AWS service that makes it easy to add image analysis to your applications. The latest feature added to the API for this deep-learning-powered computer vision is Celebrity Recognition. This simple-to-use functionality detects and recognizes thousands of individuals who are famous, noteworthy, or prominent in their field. Users can harness the tool […]

Millions of customers shop in brick and mortar stores every day. Currently, most of these retailers have no efficient way to identify these shoppers and understand their purchasing behavior. They rely on third-party market research firms to provide customer demographic and purchase preference information.

This blog post walks you how you can use AWS services to identify purchasing behavior of your customers. We show you:

How retailers can use captured images in real time.
How HAQM Rekognition can be used to retrieve face attributes like age range, emotions, gender, etc.
How you can use HAQM Athena and HAQM QuickSight to analyze the face attributes.
How you can create unique insights and learn about customer emotions and demographics.
How to implement serverless architecture using AWS managed services.

HAQM Rekognition is a service that makes it easy to add image analysis to your applications. It’s based on the same proven, highly scalable, deep learning technology developed by HAQM’s computer vision scientists to analyze billions of images daily for HAQM Prime Photos. Facial recognition enables you to find similar faces in a large collection […]

This guest post is by AWS Community Hero Cyrus Wong. Cyrus is a Data Scientist at the Hong Kong Vocational Education (Lee Wai Lee) Cloud Innovation Centre. He has achieved all 7 AWS Certifications and enjoys sharing his AWS knowledge with others through open-source projects, blog posts, and events. HowWhoFeelInVideo is an application that analyzes […]

Imagine capturing frames off of live video streams, identifying objects within the frames, and then triggering actions or notifications based on the identified objects. Now imagine accomplishing all of this with low latency and without a single server to manage In this post, I present a serverless solution that uses HAQM Rekognition and other AWS […]