• Searchable image and video libraries – Amazon Rekognition makes images and stored videos searchable so you can discover objects and scenes that appear within them.
• 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 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 images, stored videos, and streaming videos for faces that match those stored in a container known as a face collection. 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.
• Unsafe Content Detection – Amazon Rekognition can detect explicit and suggestive adult content in images and in videos. 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 and in videos. Rekognition can recognize thousands of celebrities across a number of categories, such as politics, sports, business, entertainment, and media.
• Text detection – Amazon Rekognition Text in Image allows you to recognize and extract textual content from images. Text in Image supports most fonts including highly stylized ones. It detects text and numbers in different orientations such as those commonly found in banners and posters. In image sharing and social media applications, you can use it to enable visual search based on an index of images that contain the same keywords. In media and entertainment applications, you can catalogue videos based on relevant text on screen, such as ads, news, sport scores, and captions. Finally, in security and surveillance applications, you can identify vehicles based on license plate numbers from images taken by street cameras.

• Integrate powerful image and video recognition into your apps – Amazon Rekognition removes the complexity of building image recognition capabilities into your applications by making powerful and accurate analysis available with a simple API. You don’t need computer vision or deep learning expertise to take advantage of Rekognition’s reliable image and video analysis. With Rekognition’s API, you can easily and quickly build image and video analysis into any web, mobile or connected device application.
• Deep learning-based image and video analysis – Rekognition uses deep learning technology to accurately analyze images, find and compare faces in images, and detect objects and scenes within your images and videos.
• 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 pay for the images and videos 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.

Amazon Rekognition: How It Works

Amazon Rekognition provides two API sets; Rekognition Image for analyzing images and Rekognition Video for analyzing videos.

Both API perform detection and recognition analysis of images and videos to provide insights you can use in your applications. For example, you could use Rekognition Image to enhance the customer experience for a photo management application. When a customer uploads a photo, your application can use Rekognition Image to detect real-world objects or faces in the image. After your application stores the information returned from Rekognition Image, the user could then query their photo collection for photos with a specific object or face. Deeper querying is possible. For example, the user could query for faces that are smiling or query for faces that are a certain age.

For video applications, You could use Rekognition Video to create a surveilance application. Rekognition Video can track where a person is detected throughout a stored video. Alternatively, you can use Rekognition Video to search a streaming video for persons whose facial descriptions match facial descriptions already stored by Amazon Rekognition.

The Amazon Rekognition API makes deep learning image analysis easy to use. For example,RecognizeCelebrities returns information for up to 100 celebrities detected in an image. This includes information about where celebrity faces are detected on the image and where to get further information about the celebrity.

The following information covers the types of analysis that Amazon Rekognition provides and an overview of Rekognition Image and Rekognition Video operations. Also covered is the difference between non-storage and storage operations.