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Amazon Rekognition

cloud-based Software as a service computer vision platform

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Record originEnglish Wikipedia
Text licenseCC BY-SA 4.0
Source revisionMar 15, 2026
Entity authorityQ74235852
Source-derived summary

Amazon Rekognition is a cloud-based software as a service (SaaS) computer vision platform that was launched in 2016. It has been sold to, and used by, a number of United States government agencies, including U.S. Immigration and Customs Enforcement (ICE) and Orlando, Florida police, as well as private entities.

Capabilities

Rekognition provides a number of computer vision capabilities, which can be divided into two categories: Algorithms that are pre-trained on data collected by Amazon or its partners, and algorithms that a user can train on a custom dataset.

As of July 2019, Rekognition provides the following computer vision capabilities.

Pre-trained algorithms

Celebrity recognition in images

Facial attribute detection in images, including gender, age range, emotions (e.g. happy, calm, disgusted), whether the face has a beard or mustache, whether the face has eyeglasses or sunglasses, whether the eyes are open, whether the mouth is open, whether the person is smiling, and the location of several markers such as the pupils and jaw line.

People Pathing enables tracking of people through a video. An advertised use-case of this capability is to track sports players for post-game analysis.

Text detection and classification in images

Unsafe visual content detection

Algorithms that a user can train on a custom dataset

SearchFaces enables users to import a database of images with pre-labeled faces, to train a machine learning model on this database, and to expose the model as a cloud service with an API. Then, the user can post new images to the API and receive information about the faces in the image. The API can be used to expose a number of capabilities, including identifying faces of known people, comparing faces, and finding similar faces in a database.

Editorial summary

Begin with the source’s own compact description: “Amazon Rekognition” is cloud-based Software as a service computer vision platform. The dossier treats that line as a proposition to test through Amazon, Rekognition and cloud-based, not as a finished interpretation.

Editorial reviewA sound reference starting point where classification, measurement and the date of the underlying evidence remain visible. The current lead gives the account dated anchors—2016, 2019—that can be checked directly. The linked authority record independently contributes the date 2016-11-30. For this dossier, Amazon, Rekognition and cloud-based is the immediate research focus.
Editorial analysis

Why this record matters

The phrase “cloud-based Software as a service computer vision platform” supplies a clear boundary for inquiry. It also exposes the unanswered questions: who defined that boundary, when it became stable and which sources sit outside it.

Evidence profile

Stable identifiers, scientific names and standards terminology offer the best bridge between this overview and specialist evidence. The source revision retrieved here is dated Mar 15, 2026. The linked authority identifier is Q74235852. 1 of 2 selected statements include explicit references; 1 carry qualifiers and 0 use preferred rank. The first chronological checks are 2016 and 2019.

Critical limits

Scientific names, classifications and consensus can change while older terminology persists in catalogues and historical literature. The lead is largely declarative, so disagreement and counter-evidence require a deliberate search beyond the opening account. Authority statements aid reconciliation but still require their own references, qualifiers and ranks to be checked.

How to read it

Check terminology, classification and the date of the cited evidence. Scientific names and technical consensus can change while older records retain historical value.

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  • Classification context
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Three-step research path

  1. Establish the record: confirm the title “Amazon Rekognition”, its source revision and the description used here.
  2. Expand the search: follow Amazon Rekognition primary sources, Amazon Rekognition archive and Amazon research across catalogues and specialist indexes.
  3. Test the account: compare the strongest cited source with the responsible institution’s current record and note any disagreement.

Questions for further research

  1. Which source most directly establishes the central claim about “Amazon Rekognition”?
  2. Which observation, specimen, dataset or publication supports the account?
  3. Is the terminology current, historical or disputed?
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Source & attribution

This entry incorporates text from Amazon Rekognition” on English Wikipedia. Contributors are listed in the page history. Text is available under the Creative Commons Attribution-ShareAlike 4.0 License. Selected authority identifiers and statements are retrieved from Wikidata under CC0; their references and qualifiers remain part of the verification path.