Amazon Rekognition Face Liveness

Detect real users and deter bad actors using spoofs in seconds during facial verification

Amazon Rekognition Face Liveness verifies that only real users, not bad actors using spoofs, can access your services. Amazon Rekognition Face Liveness analyzes a short selfie video to detect spoofs presented to the camera, such as printed photos, digital photos, digital videos, or 3D masks, as well as spoofs that bypass the camera, such as pre-recorded or deepfake videos. Face Liveness is a fully managed feature that can be easily added to your React web, native iOS, and native Android applications running on most devices with a front-facing camera. No infrastructure management, hardware-specific implementation, or machine learning expertise is required. The feature automatically scales up or down in response to demand, and you only pay for the face liveness checks you perform.

Validate live users from spoof attacks with Amazon Rekognition Face Liveness (2:35)

Use Cases

User onboarding

Reduce fraudulent account creation on your service by validating new users with Face Liveness. For example, financial services customers can use Face Liveness and Amazon Rekognition Face Matching to verify user identity prior to opening an online account.

Step-up authentication

Strengthen the verification of high-value user activities, such as device change, password change, and money transfer with Face Liveness. For example, ride-sharing customers can use Face Liveness and Face Matching to verify driver identity before commencing a ride.

User age verification

Deter underage users from accessing restricted content with Face Liveness. For example, online gaming or dating customers can use Face Liveness and age estimation from Amazon Rekognition Facial Analysis to verify user’s age before granting access.

Bot detection

Avoid bots from using your service with Face Liveness. For example, social media customers can use Face Liveness for posing real human checks to keep bots at bay.

How it works

How Redshift data sharing works


 High Security Presentation attacks detection Detects spoof attacks presented to the camera, such as printed 2D photos, 2D cut-out paper masks, and hi-res photos or videos on a digital screen.

Bypass attacks detection

Detects spoof attacks that bypass the camera, such as pre-recorded, synthetic, and deepfake videos directly injected into the video capture sub-system.

3D mask attacks detection

Detects spoof attacks that use 3D masks made of silicone, latex, plastic, cloth, and more.

Configurable confidence score 

Provides a confidence score between 0 and 100 for adjustable security levels based on your use case.
Low user friction

Near passive user action

Requires simple action of moving face into an oval rendered on user's device screen similar to taking a selfie video.

Fast verification

Analyzes user selfie video in real-time to minimize end-to-end latency and deliver results in seconds.

User guidance and feedback

Provides on-screen instructions and contextual guidance to help end-users complete liveness checks quickly.

Accessibility compliance

Adheres to Web Content Accessibility Guidelines (WCAG) 2.1 for the colored screens displayed in the Face Liveness user challenge, minimizing impact to photo-sensitive users.
Ease of integration

Broad platform support

Integrates into AWS Amplify SDKs for web (React) and mobile (native iOS and native Android). No hardware-specific implementation required.

Pre-built user interface (UI) components

Provides pre-built user interfaces to quickly integrate Face Liveness into your application.

Optimized video capture and streaming

Minimizes selfie video size to under 1 MB for efficient data transfer.

Selfie frame with quality checks

Provides a high-quality selfie frame for face matching or age estimation.
Ease of management

Fully managed

No need to deploy or manage liveness sotware in their on-premises or hosted infrastructure. 

Audit Image

Returns up to four frames for manual inspection or audit trail purposes.

Pay per use and Autoscaling

Pay per liveness check and automatically scale up to millions of liveness checks per day.

Open-source device SDKs

Full transparency and visibility into AWS Amplify SDKs.



Entersekt is a leading provider of strong device identity and customer authentication software. Financial institutions and other large enterprises in countries across the globe rely on its multi-patented technology to communicate with their clients securely, protect them from fraud, and serve them with convenient new experiences, irrespective of the channel or device in use.

"Our aim is to assist organizations in combating fraud, expediting genuine customer onboarding, and complying with regulatory requirements using a range of techniques, including facial biometrics. Through our evaluations, we found that Amazon Rekognition Face Liveness stands out for its user-friendly interface and exceptional precision in detecting sophisticated spoof attacks. Incorporating Rekognition Face Liveness into our identity verification processes has been easy and quick."
AU Small Finance Bank

Software Colombia is a top-tier software development company based in Bogotá, Colombia, providing cutting-edge technology solutions globally.

"AWS identity verification and its new Amazon Rekognition Face Liveness helped our new eLogic biometrical solution reduce fraud and risk by 95%, while making our product more inclusive and accessible.”

Alex Chacón, Software Colombia CEO


What AWS regions are supported?
Face Liveness is available in five AWS regions - US East (N. Virginia), US West (Oregon), Europe (Ireland), Asia Pacific (Tokyo), and Asia Pacific (Mumbai). To learn more about the region support, visit the Amazon Rekognition endpoints page.

What are the outputs from Face Liveness?
Face Liveness feature produces a probabilistic confidence score, ranging from 0 to 100. A higher score corresponds to a higher confidence that the user is live and real. Face Liveness provides a selfie frame for face matching or age estimation. The feature also returns up to four audit frames for human review and audit trail purposes.

Should I use Face Liveness to replace username and passwords?
We do not recommend using Face Liveness and Face Matching in place of username/password. We recommend using Face Liveness and Face Matching as a secondary or step-up method to username/password for an additional layer of security.

How do you address bias in Face Liveness?
Face Liveness is trained and tested using datasets that represent a diverse range of human facial features and skin tones under a wide range of environmental variations. This includes datasets of selfie videos for which we have reliable demographic labels such as gender, age, and skin tone.

Are user videos processed by Face Liveness stored? How can I opt-out of data storage?
Face Liveness may store and use selfie video processed solely to provide, maintain, and improve the feature, unless you opt out. To learn more, visit the Rekognition Data Privacy.

How is Face Liveness priced?
Face Liveness is priced on a per check basis. To learn more, visit the Amazon Rekognition pricing page

Learn more about Amazon Rekognition pricing

Visit the pricing page
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