Amazon Rekognition
Developer Guide

What Is Amazon Rekognition?

Amazon Rekognition is a service that enables you 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. The Rekognition API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications. Rekognition is built to analyze images at scale and integrates seamlessly with Amazon S3, AWS Lambda, and other AWS services.

Common use cases for using Amazon Rekognition include the following:

  • Searchable image library – Amazon Rekognition makes images searchable so you can discover objects and scenes that appear within them. You can create an AWS Lambda function that automatically adds newly detected image labels directly into an Amazon Elasticsearch Service search index when a new image is uploaded into Amazon S3.


  • Face-based user verification – Amazon Rekognition enables your applications to confirm user identities by comparing their live image with a reference image.


  • Sentiment and demographic analysis – Amazon Rekognition detects emotions such as happy, sad, or surprise, and demographic information such as gender from facial images. Rekognition can analyze live images, and send the emotion and demographic attributes to Amazon Redshift for periodic reporting on trends such as in store locations and similar scenarios.


  • Facial recognition – With Amazon Rekognition, you can search your image collection for similar faces by storing faces, using the IndexFaces API operation. You can then use the SearchFaces operation to return high-confidence matches. A face collection is an index of faces that you own and manage. Identifying people based on their faces requires two major steps in Amazon Rekognition:

    1. Index the faces.

    2. Search the faces.


  • Image moderation – Amazon Rekognition can detect explicit and suggestive adult content in images. Developers can use the returned metadata to filter inappropriate content based on their business needs. Beyond flagging an image based on the presence of adult content, the API also returns a hierarchical list of labels with confidence scores. These labels indicate specific categories of adult content, thus allowing granular filtering and management of large volumes of user generated content (UGC). For example, social and dating sites, photo sharing platforms, blogs and forums, apps for children, e-commerce sites, entertainment and online advertising services.


  • Celebrity recognition – Amazon Rekognition can recognize celebrities within supplied images. Rekognition can recognize thousands of celebrities across a number of categories, such as politics, sports, business, entertainment, and media.

Some of the benefits of using Amazon Rekognition include:

  • Integrate powerful image recognition into your apps – Amazon Rekognition removes the complexity of building image recognition capabilities into your applications by making powerful and accurate image analysis available with a simple API. You don’t need computer vision or deep learning expertise to take advantage of Rekognition’s reliable image analysis. With Rekognition’s API, you can easily and quickly build image analysis into any web, mobile or connected device application.


  • Deep learning-based image analysis – Rekognition uses deep learning technology to accurately analyze images, find and compare faces, and detect objects and scenes within your images.


  • Scalable image analysis – Amazon Rekognition enables you to analyze millions of images so you can curate and organize massive amounts of visual data.


  • Integrate with other AWS services – Amazon Rekognition is designed to work seamlessly with other AWS services like Amazon S3 and AWS Lambda. Rekognition’s API can be called directly from Lambda in response to Amazon S3 events. Since Amazon S3 and Lambda scale automatically in response to your application’s demand, you can build scalable, affordable, and reliable image analysis applications. For example, each time a person arrives at your residence, your door camera can upload a photo of the visitor to Amazon S3, triggering a Lambda function that uses Rekognition API operations to identify your guest. You can run analysis directly on images stored in Amazon S3 without having to load or move the data. Support for AWS Identity and Access Management (IAM) makes it easy to securely control access to Rekognition API operations. Using IAM, you can create and manage AWS users and groups to grant the appropriate access to your developers and end users.


  • Low cost – With Amazon Rekognition, you only pay for the number of images you analyze and the face metadata that you store. There are no minimum fees or upfront commitments. Get started for free, and save more as you grow with Rekognition's tiered pricing model.

Are You a First-Time Amazon Rekognition User?

If you are a first-time user of Amazon Rekognition, we recommend that you read the following sections in order:

  1. Amazon Rekognition: How It Works – This section introduces various Amazon Rekognition components that you work with to create an end-to-end experience.

  2. Getting Started with Amazon Rekognition – In this section you set your account and test the Amazon Rekognition API.

  3. Additional Amazon Rekognition Examples – This section provides additional examples that you can use to explore Amazon Rekognition.