Face liveness
Checks whether a user is a real person rather than a spoof during facial verification workflows, with results available in seconds according to the product page.
Amazon Rekognition is an AWS computer vision service for analyzing images, video streams, and stored video with pretrained or customizable machine learning APIs. It helps application and content teams add visual analysis without building ML models and infrastructure from scratch.
Amazon Rekognition is a managed AWS computer vision service for analyzing images, video streams, and stored videos. It provides pretrained and customizable machine learning APIs so teams can add visual analysis without building models and infrastructure from scratch.
The service supports workflows involving face analysis, identity verification, object and scene detection, text extraction, content moderation, video analysis, and application-specific object recognition. It is designed for applications and operational teams that need to process visual media and augment review or automation workflows.
Checks whether a user is a real person rather than a spoof during facial verification workflows, with results available in seconds according to the product page.
Detects faces in images and videos and analyzes attributes such as open eyes, glasses, and facial hair.
Uses AutoML to train recognition models for application-specific objects, including examples such as brand logos, with as few as 10 images cited by AWS.
Extracts text from skewed or distorted imagery, including street signs, social media posts, and product packaging.
Identifies potentially unsafe content as well as objects, scenes, activities, landmarks, dominant colors, and image quality.
Detects video segments such as black frames, credits, slates, color bars, and shots in stored video.
Screen uploaded images and videos for potentially unsafe, inappropriate, or unwanted material according to a platform's general or business-specific standards.
Combine face comparison and face analysis with opted-in onboarding or authentication workflows to remotely verify a user's identity.
Find key segments in stored video to reduce manual effort when preparing footage for advertising, content operations, or production workflows.
Analyze live video streams for desired objects and use detections to trigger timely alerts or home-automation actions, such as turning on a light when a person is detected.
Train Custom Labels models for domain-specific objects, such as transformer damage in drone footage, when standard labels do not cover the target.
It is used to analyze images, video streams, and stored videos for tasks such as face analysis, identity verification, text detection, content moderation, object recognition, and video segment detection.
AWS positions Rekognition as a way to add pretrained or customizable computer vision APIs without building machine learning models and infrastructure from scratch. Custom application-specific recognition still requires supplying example images and defining an appropriate workflow.
Yes. The product page explicitly describes analysis of video streams as well as stored videos. Its connected-home example uses live streams to detect desired objects and trigger alerts or automation.
AWS provides a product-specific Rekognition pricing page, while the general AWS pricing model is primarily pay-as-you-go for services consumed. The supplied evidence does not state Rekognition's current billing units or prices, so those details should be checked directly before deployment.
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