| Facial liveness detection protects face biometric systems from spoofing attacks. It does this by determining if the person in front of the camera is present (live) or someone presenting a printed photo, video, digital image, or using a mask to trick the system into thinking it sees the real person.
With large organizations processing millions of face checks monthly, one advantage of AI liveness detection is the reduced burden on humans. Another advantage of AI is faster response times for the customer. But these advantages aside, which is better at the job, the human or AI?
The team at ID R&D tackled this question with an experiment involving 175,454 images across five types of presentation attacks, including printed photos, digital displays, printed cutouts, 2D masks, and 3D masks. In this webinar hosted in partnership with Biometric Update, ID R&D CEO Alexey Khitrov will share the research and dive deeper into the methodology and important takeaways from the findings including:
- Why the research was needed
- How the research was conducted
- Examples of spoofing attacks used in the experiment
- A closer look at the data
- How to improve the accuracy of liveness detection
- What the findings mean for your business
Alexey will end with a Q&A and a preview of additional research the team is conducting. |
