{"id":184048,"date":"2026-06-12T12:27:36","date_gmt":"2026-06-12T12:27:36","guid":{"rendered":"https:\/\/flypix.ai\/?p=184048"},"modified":"2026-06-12T12:27:37","modified_gmt":"2026-06-12T12:27:37","slug":"amazon-rekognition-tool-review","status":"publish","type":"post","link":"https:\/\/flypix.ai\/es\/amazon-rekognition-tool-review\/","title":{"rendered":"Descripci\u00f3n general de la herramienta Amazon Rekognition 2026: caracter\u00edsticas y precios"},"content":{"rendered":"

Resumen r\u00e1pido: <\/strong>Amazon Rekognition is AWS’s fully managed computer vision service that uses deep learning to analyze images and videos without requiring machine learning expertise. It offers pre-trained APIs for face detection, object recognition, text extraction, content moderation, and custom label training. The service scales automatically and charges based on volume, starting at $0.00001 per image for the first million processed.<\/p>\n\n\n\n

Amazon Rekognition launched in 2016 as AWS’s answer to the growing need for accessible computer vision technology. It’s designed to solve a specific problem: most developers need image and video analysis capabilities but don’t have the time, budget, or expertise to build machine learning models from scratch.<\/p>\n\n\n\n

The service runs entirely in the cloud. You send images or videos through API calls, and Rekognition returns structured data about what it detected\u2014faces, objects, text, inappropriate content, whatever you’re looking for.<\/p>\n\n\n\n

Here’s the thing though\u2014Rekognition isn’t one monolithic tool. It’s actually a suite of specialized APIs that each handle different computer vision tasks.<\/p>\n\n\n\n

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Core Capabilities and API Categories<\/h2>\n\n\n\n

Rekognition divides its features into two main categories: image analysis and video analysis. Each category contains multiple specialized APIs.<\/p>\n\n\n\n

Image Analysis Features<\/h3>\n\n\n\n

The image analysis side handles static images. According to the official documentation, objects and faces must be at least 5% of the shorter image dimension for reliable detection. For a 1600×900 pixel image, that translates to a minimum of 45 pixels.<\/p>\n\n\n\n

Label Detection identifies objects, scenes, activities, and concepts within images. It returns confidence scores for each detection, typically ranging from 0-100. Content Moderation scans for inappropriate or unwanted content across multiple categories.<\/p>\n\n\n\n

Face Detection and Analysis can identify faces in images and extract attributes like estimated age range, perceived gender, emotions, and whether the person appears to be wearing glasses. Face Comparison measures similarity between two faces, while Face Search matches detected faces against a stored collection.<\/p>\n\n\n\n

Text Detection extracts printed and handwritten text from images\u2014useful for document processing, street sign recognition, and similar applications.<\/p>\n\n\n\n

Video Analysis Features<\/h3>\n\n\n\n

Video analysis works differently. You can process stored videos asynchronously or analyze streaming video in real-time. The system detects people, tracks their movement, recognizes objects and activities, and identifies when scenes change.<\/p>\n\n\n\n

For segment detection, you can filter results by confidence threshold. A 70% minimum confidence filter is commonly used to balance accuracy and coverage.<\/p>\n\n\n\n

But wait. As of April 30, 2026, Amazon discontinued offering Streaming Video Analysis and Batch Image Content Moderation to new customers. Existing users who’ve accessed these features within the last 12 months retain access, but new accounts cannot enable them.<\/p>\n\n\n\n

\"Amazon<\/figure>\n\n\n\n

Face Liveness Detection<\/h2>\n\n\n\n

Face Liveness is Rekognition’s answer to spoofing attacks. The feature analyzes short selfie videos to determine whether a real person is present or if someone is using a photo, pre-recorded video, 3D mask, or deepfake.<\/p>\n\n\n\n

It detects presentation attacks (printed photos, digital screens, paper masks shown to the camera) and bypass attacks (pre-recorded or synthetic videos injected into the video stream). The system returns a configurable confidence score from 0-100.<\/p>\n\n\n\n

This functionality integrates with React web applications, native iOS apps, and native Android apps. No infrastructure management required\u2014it’s fully managed by AWS.<\/p>\n\n\n\n

Custom Labels for Specialized Recognition<\/h2>\n\n\n\n

The pre-trained models work well for general use cases. But what about specialized scenarios\u2014detecting specific vehicle models, identifying manufacturing defects, or recognizing proprietary products?<\/p>\n\n\n\n

That’s where Custom Labels come in. You provide training images labeled with your custom categories, and Rekognition builds a model tailored to your specific recognition needs. Recent improvements mean you can now build quality models with less training data than previously required.<\/p>\n\n\n\n

Seven new APIs now support programmatic model creation, dataset management, and training workflow automation.<\/p>\n\n\n\n

Apply Geospatial Image Analysis with FlyPix AI<\/h2>\n\n\n\n

Amazon Rekognition is used for image and video analysis across general computer vision tasks. FlyPix AI<\/a> works in a more specific area, helping teams analyze satellite, drone, and aerial imagery to detect objects, segment locations, and monitor visible changes across real-world sites.<\/p>\n\n\n\t\t

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Precios<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\t\t\tPricing in \u20ac EUR\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Inicio<\/strong>
Almacenamiento
10 GB<\/strong>
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100 \u20ac\/usuario\/mes<\/b><\/span>
50 cr\u00e9ditos<\/strong>
~1 Gigap\u00edxel<\/span><\/div><\/div><\/div>
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Inscribirse<\/a><\/div><\/div><\/div><\/div><\/div>
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