Amazon Rekognition

Name: Amazon Rekognition API
Brand: Amazon Rekognition
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLVisionhmac65 EndpointsREST

For Agents

Detect labels, faces, text, celebrities, and unsafe content in images and video, and search face collections through Amazon Rekognition.

Connect the Amazon Rekognition to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Rekognition, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Amazon Rekognition API.

Detect labels, scenes, and objects in an image with bounding boxes and confidence via DetectLabels

Compare two faces and return a similarity score using CompareFaces for identity verification

Search a face collection for matches against a probe image with SearchFacesByImage

Run text-in-image OCR with DetectText, returning words, lines, and bounding geometry

GET STARTED

Start building with Amazon Rekognition API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Amazon Rekognition, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon Rekognition, keeping it validated and agent-ready. Rekognition is a managed computer-vision service for image and video understanding: face detection, comparison, and search; object, scene, and label detection; text-in-image (OCR); content moderation; celebrity recognition; PPE detection; and stream-based video analysis. The API surfaces 65 operations across synchronous image analysis, asynchronous video jobs, face-collection management, custom Rekognition Custom Labels project lifecycle, and live-stream processors. It is the canonical AWS API for adding vision intelligence to applications.

Use Cases

Patterns agents use Amazon Rekognition API for, with concrete tasks.

★ Identity Verification with Face Match

Verify that a selfie matches a stored ID photo by calling CompareFaces with both image bytes (or S3 object references) and reading the returned Similarity score. Pair with CreateFaceLivenessSession and GetFaceLivenessSessionResults to defend against spoofing using printed photos or replay video. Used in onboarding flows for fintech, ride-share, and marketplaces.

Call CompareFaces with SourceImage of the selfie bytes and TargetImage of the stored ID photo, then return the FaceMatch with the highest Similarity

Image Content Moderation

Filter user-generated content by calling DetectModerationLabels on each upload. Rekognition returns a hierarchy of moderation labels (e.g., 'Suggestive' > 'Female Swimwear or Underwear') with confidence scores so policy engines can block, age-gate, or human-review accordingly. Combine with DetectLabels and DetectText for richer signals.

Call DetectModerationLabels with Image.S3Object pointing to the uploaded image and MinConfidence 80, then route based on returned ModerationLabels

Video Label Detection at Scale

Tag long-form video assets with timestamped labels by starting an async job. StartLabelDetection takes an S3 video object and an SNS topic; when the job completes, GetLabelDetection returns labels with timestamps for every detected object or scene. Useful for media archives, ad insertion, and content discovery.

Call StartLabelDetection with Video.S3Object pointing to the source video and NotificationChannel.SNSTopicArn, then call GetLabelDetection with the returned JobId once the SNS message arrives

AI Agent Vision Tool via Jentic

A multimodal agent uses Jentic to discover Rekognition operations such as DetectLabels and DetectText, load their schemas, and call them when a tool route requires image understanding. Jentic stores AWS credentials in its vault and signs each call with SigV4, so the agent only handles the image bytes (or S3 reference) and the structured response.

Use Jentic to search 'detect labels in an image with rekognition', load DetectLabels, and execute it with the user's image bytes

Key Endpoints

65 endpoints — jentic publishes the only available openapi specification for amazon rekognition, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/#X-Amz-Target=RekognitionService.DetectLabels

Detect labels, scenes, and objects in an image

POST

/#X-Amz-Target=RekognitionService.CompareFaces

Compare two faces and return a similarity score

POST

/#X-Amz-Target=RekognitionService.DetectText

Run OCR and extract words and lines from an image

POST

/#X-Amz-Target=RekognitionService.DetectModerationLabels

Identify unsafe or moderated content categories in an image

POST

/#X-Amz-Target=RekognitionService.IndexFaces

Add faces from an image to a face collection

POST

/#X-Amz-Target=RekognitionService.SearchFacesByImage

Search a face collection for matches to a probe image

POST

/#X-Amz-Target=RekognitionService.StartLabelDetection

Start an asynchronous video label-detection job

POST

/#X-Amz-Target=RekognitionService.GetLabelDetection

Retrieve completed video label-detection results by JobId

POST

/#X-Amz-Target=RekognitionService.DetectLabels

Detect labels, scenes, and objects in an image

POST

/#X-Amz-Target=RekognitionService.CompareFaces

Compare two faces and return a similarity score

POST

/#X-Amz-Target=RekognitionService.DetectText

Run OCR and extract words and lines from an image

POST

/#X-Amz-Target=RekognitionService.DetectModerationLabels

Identify unsafe or moderated content categories in an image

POST

/#X-Amz-Target=RekognitionService.IndexFaces

Add faces from an image to a face collection

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

AWS access keys are stored encrypted in the Jentic vault. SigV4 signatures are produced per request, so agents never hold the raw secret access key.

Intent-based discovery

Agents search by intent (e.g., 'detect labels in an image with rekognition') and Jentic returns DetectLabels with its input schema for Image, MinConfidence, and Features.

Time to first call

Direct integration: 1-2 days to wire up SDK, IAM scoping, S3 access patterns for image and video inputs, and async-job result handling. Through Jentic: under 30 minutes for the synchronous image operations.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Amazon Textract

Textract specializes in document OCR, forms, and tables; Rekognition handles general scene text and broader visual analysis

Use Textract for structured document extraction (forms, tables, key-value pairs); use Rekognition.DetectText for free-form text in scenes and images.

Complementary

Amazon Comprehend

Apply NLP (entities, sentiment, classification) to text extracted from images by Rekognition

Use Comprehend after Rekognition.DetectText to understand the meaning, language, or sentiment of the recovered text.

Complementary

Amazon Polly

Narrate Rekognition's image descriptions for accessibility and audio-described media

Use Polly to convert image labels and captions produced by Rekognition into spoken audio for screen-reader or audio-tour experiences.

FAQs

Specific to using Amazon Rekognition API through Jentic.

Why is there no official OpenAPI spec for Amazon Rekognition?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Rekognition via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Rekognition API use?

Rekognition uses AWS SigV4 HMAC request signing with an AWS access key ID and secret. Through Jentic, AWS credentials are stored in the vault and SigV4 signatures are produced per request, so the agent never sees raw secrets.

Can I run Rekognition on video as well as images?

Yes. Synchronous image operations (DetectLabels, DetectFaces, DetectText, etc.) accept Image bytes or an S3 object. Video operations are asynchronous: call StartLabelDetection, StartFaceSearch, StartContentModeration, or StartCelebrityRecognition with a Video.S3Object and NotificationChannel, then call the matching Get* operation with the returned JobId.

What image formats and sizes are supported?

Rekognition accepts JPEG and PNG. Synchronous calls cap image bytes at 5 MB when passed inline and 15 MB when referenced via S3. Minimum face size for detection is roughly 40x40 pixels. Videos must be H.264 in MP4 or MOV containers and live in S3.

What are the rate limits for Rekognition?

Rekognition enforces per-region transactions-per-second limits per operation; defaults vary (DetectLabels often starts around 50 TPS) and quota increases can be requested. Throttled calls return ProvisionedThroughputExceededException — back off and retry.

How do I detect labels in an image through Jentic?

Search Jentic for 'detect labels in an image with rekognition', load DetectLabels, and execute it with Image bytes (or Image.S3Object) and MinConfidence. Install with pip install jentic; AWS credentials are pulled from the Jentic vault.