Amazon Rekognition

Automate your image and video analysis with machine learning.

Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.

With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required. You simply need to supply images of objects or scenes you want to identify, and the service handles the rest.

Customers

National Football League
CBS
National Geographic
Marinus Analytics
SmugMug
Sky News

Key features

Object, scene, and activity detection

Labels

With Amazon Rekognition, you can identify thousands of objects (such as bike, telephone, building), and scenes (such as parking lot, beach, city). When analyzing video, you can also identify specific activities such as "delivering a package" or "playing soccer".

Object, scene, and activity detection

Custom labels

With Amazon Rekognition Custom Labels, you can extend the detection capabilities of Amazon Rekognition to extract information from images that is uniquely helpful to your business. For example, you can find your corporate logo in social media, identify your products on store shelves, classify your machine parts in an assembly line, or detect your animated characters in videos.

Unsafe content detection

Content moderation

Amazon Rekognition helps you identify potentially unsafe or inappropriate content across both image and video assets and provides you with detailed labels that allow you to accurately control what you want to allow based on your needs.

 

Facial analysis

Text detection

In photos, text appears very differently than neat words on a printed page. Amazon Rekognition can read skewed and distorted text to capture information like store names, street signs, and text on product packaging.

Facial analysis

Face detection and analysis

With Amazon Rekognition, you can easily detect when faces appear in images and videos and get attributes such as gender, age range, eyes open, glasses, facial hair for each. In video, you can also measure how these face attributes change over time, such as constructing a timeline of the emotions expressed by an actor.

 

Facial recognition

Face search and verification

Amazon Rekognition provides fast and accurate face search, allowing you to identify a person in a photo or video using your private repository of face images. You can also verify identity by analyzing a face image against images you have stored for comparison.

 

 

Celebrity recognition

Celebrity recognition

You can quickly identify well known people in your video and image libraries to catalog footage and photos for marketing, advertising, and media industry use cases.

 

Pathing

Pathing

You can capture the path of people in the scene when using Amazon Rekognition with video files. For example, you can use the movement of athletes during a game to identify plays for post-game analysis.

 

Customer success

NFL
“In today’s media landscape, the volume of unstructured content that organizations manage is growing exponentially. Using traditional tools users can have difficulty in searching through the thousands of media assets in order to locate a specific element they are looking for. By using the new feature in Amazon Rekognition, Custom Labels, we are able to automatically generate metadata tags tailored to specific use cases for our business and provide searchable facets for our content creation teams. This significantly improves the speed in which we can search for content and more importantly it enables us to automatically tag elements that required manual efforts before. These tools allow our production teams to leverage this data directly and provides enhanced products to our customers across all of our media platforms.“

Brad Boim, Senior Director, Post Production & Asset Management, NFL Media


CBS

CBS Corporation is a mass media company that creates and distributes industry-leading content across a variety of platforms globally. CBS owns the most-watched television network in the U.S. and one of the world’s largest libraries of entertainment content, making its brand — “the Eye” — one of the most recognized in business.

"At CBS, we place significant efforts to ensure we moderate inappropriate content within our programming as to not offend our global viewers or violate government regulations. This is supported by investments in manual methods to execute near real-time screening and editing of hundreds of hours of content every month. To scale our internal processes, we are looking to Amazon Rekognition to automate the moderation of our video content while leveraging the new feature of Custom Labels to further refine moderation models. This will enable us to automate the tagging of sensitive content such as nudity, obscene gestures, and violence, and speed up processing from hours to minutes."

Jamie Duemo, Senior Vice President, MultiPlatform Distribution - CBS Operations and Engineering


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Influential is a premier AI powered influencer marketplace. Influential eliminates the pain point of identifying influencers by leveraging AI and machine learning to suggest influencers through actionable insights and predictive intelligence.

"In addition to our in-house AI/ML algorithms, we partner with third parties to enrich our datasets in order to better facilitate influencer sourcing. Amazon Rekognition object and scene detection allows us to better segment our influencer population into specific verticals and topics based on what media they post alongside their social media content. By extending our search capabilities beyond just text, we allow for better training of our Brand Match Score, which when combined with Rekognition’s user-friendly tags & labels increase our hit-rate on user queries by over 200%."

Piotr Tomasik, CTO - Influential


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Marinus Analytics provides law enforcement with tools, founded in artificial intelligence, to turn big data into actionable intelligence. The Marinus flagship software, Traffic Jam, is a suite of tools for use by law enforcement agencies on sex trafficking investigations.

“Law enforcement needs sophisticated tools to foster victim-oriented policing in the age of the Internet. Law enforcement knew that runaway children are among the most likely to be trafficked. Before using Amazon Rekognition, their only recourse was to manually sift through online data to try to find them; this was time-intensive or not possible. Now with Traffic Jam’s FaceSearch, powered by Amazon Rekognition, investigators are able to take effective action by searching through millions of records in seconds to find victims.”

Emily Kennedy, CEO and Founder - Marinus Analytics


aella-credit-600x400

Aella Credit provides instant loans to individuals with a verifiable source of income in emerging markets using biometric, employer, and mobile phone data.

“Identity verification and validation have been a major challenge in emerging markets. The ability to properly identify users is a key hindrance in building credit for billions of people in emerging markets. Using Amazon Rekognition for identity verification on our mobile application has reduced verification errors significantly and given us the ability to scale. We can now detect and verify an individual’s identity in real time without any human intervention, thereby allowing faster access to our products. We tried various well-advertised solutions, but none of the popular alternatives could accurately map out various skin tones. Amazon Rekognition helped us effectively recognize faces of our customers in our markets. It also helped us with KYC in discovering overlapping profiles and duplicate datasets."

Wale Akanbi, CTO & Co-Founder - Aella Credit

Use cases

Make content searchable

Amazon Rekognition automatically extracts metadata from your image and video files, capturing objects, faces, text and more. This metadata can be used to easily search your images and videos with keywords, or to find the right assets for content syndication.    

Flag inappropriate content

With Amazon Rekognition you can automatically flag inappropriate content, such as nudity, graphic violence or weapons, in images and videos. Using the detailed metadata returned, you can create your own rules based on what is considered appropriate for the culture and demographics of your users.

Enable digital identity verification

Using Amazon Rekognition, you can create scalable authentication workflows for automated payments and other identity verification scenarios. Amazon Rekognition lets you easily perform face verification for opted-in users by comparing a photo or selfie with an identifying document such a driver's license.

Respond quickly to public safety challenges

Amazon Rekognition allows you to create applications that help find missing persons in images and videos. By searching for their faces against a database of missing persons that you provide, you can accurately flag potential matches and speed up a rescue operation.

Identify products, landmarks and brands

App developers can use Amazon Rekognition Custom Labels to identify specific items in social media and photo apps. For example, you could train a custom model to identify famous landmarks in a city to provide tourists with information about its history, operating hours, and ticket prices by simply taking a photo.

Analyze shopper patterns

With Amazon Rekognition, you can analyze shopper behavior and density in your retail store by studying the path that each person follows. Using face analysis, you can also understand the average age ranges, gender distribution and emotions expressed by the people, without identifying them.

Learn more about Amazon Rekognition

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