    Amazon Rekognition Face Liveness now returns Feedback Codes 0 views Skip to first unread message Eyal Estrin unread, 9:45 AM (10 minutes ago)    to https://aws.amazon.com/about-aws/whats-new/2026/09/rekognition-liveness-feedback-codes/ https://docs.aws.amazon.com/rekognition/latest/dg/face-liveness-programming-api.html Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Learn how to build an event-driven video surveillance system on AWS that automatically filters false alarms and detects camera offline events in real time. This practical guide bridges physical security operations with serverless architecture using Kinesis Video Streams, Rekognition, and Lambda.

Amazon Rekognition Face Liveness now returns Feedback Codes that help customers understand why a liveness check received a low score and how users could retry successfully. The codes are returned in the GetFaceLivenessSessionResults API response, and they identify the specific conditions that contributed to the low score, such as poor lighting, face obstruction, closed eyes, or low video quality. Customers can surface these codes to their end users who can then make targeted corrections and retry with confidence. A single session can return multiple Feedback Codes at once, allowing users to resolve all contributing issues in one retry. This feature is especially useful in identity verification onboarding, re-authentication flows, and accessibility scenarios. Amazon Rekognition Face Liveness verifies that the person in front of the camera is a real, live human (instead of a photo, video replay, mask, or deepfake) through a short, guided selfie-video check. It returns a confidence score, a reference image for face matching, and session audit images.  Amazon Rekognition Face Liveness API documentation and Face Liveness FAQ.

Amazon Rekognition Face Liveness now returns Feedback Codes that help customers understand why a liveness check received a low score and how users could retry successfully. The codes are returned in the GetFaceLivenessSessionResults API response, and they identify the specific conditions that contributed to the low score, such as poor lighting, face obstruction, closed eyes, or low video quality. Customers can surface these codes to their end users who can then make targeted corrections and retry with confidence. A single session can return multiple Feedback Codes at once, allowing users to resolve all contributing issues in one retry. This feature is especially useful in identity verification onboarding, re-authentication flows, and accessibility scenarios. Amazon Rekognition Face Liveness verifies that the person in front of the camera is a real, live human (instead of a photo, video replay, mask, or deepfake) through a short, guided selfie-video check. It returns a confidence score, a reference image for face matching, and session audit images.  Rekognition Face Liveness is available in US East (N. Virginia), US West (Oregon), Europe (Ireland), Asia Pacific (Tokyo, Mumbai, Malaysia, Thailand), and South America (São Paulo). To learn more, visit the https://docs.aws.amazon.com/rekognition/latest/dg/face-liveness-programming-api.html and https://docs.aws.amazon.com/rekognition/latest/dg/face-liveness-faq.html.

Amazon Rekognition Face Liveness now returns Feedback Codes that help customers understand why a liveness check received a low score and how users could retry successfully. The codes are returned in the GetFaceLivenessSessionResults API response, and they identify the specific conditions that contributed to the low score, such as poor lighting, face obstruction, closed eyes, or low video quality. Customers can surface these codes to their end users who can then make targeted corrections and retry with confidence. A single session can return multiple Feedback Codes at once, allowing users to resolve all contributing issues in one retry. This feature is especially useful in identity verification onboarding, re-authentication flows, and accessibility scenarios. Amazon Rekognition Face Liveness verifies that the person in front of the camera is a real, live human (instead of a photo, video replay, mask, or deepfake) through a short, guided selfie-video check. It returns a confidence score, a reference image for face matching, and session audit images.  Rekognition Face Liveness is available in US East (N. Virginia), US West (Oregon), Europe (Ireland), Asia Pacific (Tokyo, Mumbai, Malaysia, Thailand), and South America (São Paulo). To learn more, visit the Amazon Rekognition Face Liveness API documentation and Face Liveness FAQ.

Amazon Rekognition Face Liveness now returns Feedback Codes to help users understand why liveness checks failed and how to retry successfully.

Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds.

Learn how to build a conversational video intelligence solution on AWS using an agentic architecture. A single Strands Agents SDK agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, deciding which service to call so you can ask natural language questions about your videos and get answers in seconds.

YouTube video by ACLU of Northern CA

LogClass turns a single classroom photo into verified attendance in seconds. Here's how Amazon Rekognition, AWS Lambda, DynamoDB, and S3 come together in a serverless pipeline — plus the security decisions that keep AWS credentials out of the browser.

This article demonstrates how to use synthetic data augmentation with Amazon SageMaker AI and Amazon Rekognition to improve industrial safety AI models, achieving 160% improvement in person detection for edge-deployed systems.

Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This approach improved person detection by up to 160% without manual annotation or hazardous data collection near heavy machinery.

Learn how to build a synthetic data augmentation pipeline on Amazon SageMaker AI and Amazon Rekognition that generates photo-realistic, auto-labeled training images for industrial safety AI. This approach improved person detection by up to 160% without manual annotation or hazardous data collection near heavy machinery.

Leverage advanced AI/ML capabilities to host a professional API. Build a production-ready, secure, and cost-effective image analysis service using foundational AWS components and Amazon Rekognition to boost your cloud resume without breaking the bank.

Discover how the Academic League of Information Systems (LASI) structured an immersive, hands-on cloud computer vision event for university students. Learn practical logistical tips for hosting seamless AWS workshops by eliminating local setup hurdles.

Numerous studies have attempted to identify instances of human trafficking through online sex advertisements. In this paper, I critically examine why these studies fall short of their claims, highl...

About Amazon Rekognition

Skycase uses Amazon Bedrock and Rekognition so a phone camera can spot your bag on the carousel and catch a missing one early.

Amazon Bedrock, SageMaker AI, AgentCore, Textract, and Rekognition explained with simple real-world examples.