Passive liveness detection

Veriff validates that the user is real and present during the authentication session without requiring the end user to complete any additional or unnatural actions. Our liveness models are capable of detecting spoof attempts such as presentation attacks using masks or presented screens, or an AI-generated selfie.

Face matching

Artificial intelligence analyzes and compares the biometric template from the authentication session to the stored biometric template from the enrolment session to determine if it's the same user or not

Adaptive technology

AI models adapt to evolving fraud patterns, providing proactive protection against emerging threats. Using liveness detection, face blocklisting, device fingerprinting, and dynamic fraud checks creates a robust security approach without impacting user experience.

Optional background video

Optional background video recording, enables us to conduct advanced fraud prevention checks with minimal input from your users. 

Account takeover mitigation

Prevent fraudsters from unauthorized access to your users’ accounts by confirming it is the genuine account holder through a secure selfie authentication method.

Step-up authentication

Dynamically adjust the level of authentication required based on the associated risk. Seamlessly integrate biometric authentication as a step-up authentication method to authorize high-value transactions or high-risk actions.

Account recovery

Selfie-based biometric authentication provides stronger assurance that the user requesting the account recovery is the genuine account holder.

Network and device analytics

In addition to selfie checks, Veriff automatically analyzes over 1,000 data points such as network and device analytics to combat evolving fraud typologies.

Streamlined user experience  

Our fully automated solution provides a response in under a second, enabling your users to authenticate themselves instantly with a simple selfie and eliminating the burden of remembering and managing complex passwords. Users do not need to leave the journey to receive a one-time passcode or use third-party authenticator apps.

Assisted Image Capture

Veriff uses machine-learning technology to assess client sessions in real-time and guide users during the photo capture process. Clear and actionable user feedback due to poor lighting, glare, cropped ID, and obstructed facial images are just a few examples of how Assisted Image Capture pre-validates images and boosts first-time pass rates.

Cross-platform

Users can complete authentication on any device and platform of their choice that has a user-facing camera. Veriff supports iOS, Android™, mobile web, and web SDK or API.

Flexible enrolment

Depending on the use case or user journey, users can be enrolled either via Veriff’s Identity Verification solution or simply taking an enrolment selfie. The enrolled selfie is stored as a biometric template for analyzing future authentication sessions.

Integrated with IDV

Combine Veriff’s Biometric Authentication with Veriff’s Identity Verification solution to add a layer of security and assurance that the user is bound to a verified identity document. Use the selfie from the IDV enrolment for future authentication sessions.

Explainable decision making 

Veriff sends you a clear and actionable result, enabling you to make an informed final decision to allow, deny, or request additional authentication from your users.

Fully automated

Fully automated facial analysis technology delivers 1 second response time, enabling users to authenticate themselves instantly with a simple selfie.