What is Liveness Detection?
Liveness detection is a technique used to ensure that a person being verified is real, and not a photograph or a pre-recorded video. An important aspect of identification systems, liveness detection helps prevent fraud and ensure that the person being verified is who they claim to be.
Liveness detection can be implemented using various methods such as motion-based detection, thermal imaging, depth sensing, 3D facial recognition, and voice recognition. The goal of liveness detection is to increase the security of the biometric system by detecting attempts of spoofing or impersonation.
What are the types of liveness detection?
Active and passive liveness detection are the two different methods used to confirm that the person providing biometric information is a live, real human being.
- Active liveness detection requires the user to perform a specific action, such as moving their head, blinking their eyes, or speaking a phrase. The system then uses the action to confirm the user’s liveness. This method can detect if the user is a photograph, video, or a mask, as they cannot perform the specific action. Active liveness detection can be more effective than passive liveness detection, as it is harder for an attacker to replicate the specific action.
- Passive liveness detection, on the other hand, does not require the user to perform a specific action. Instead, it relies on the user’s natural movement or physiological characteristics to confirm their liveness. For example, passive liveness detection can use thermal imaging to detect the heat signature of a person’s face, or depth sensing to detect the depth of the face. This method can detect if the face is a 3D-printed mask or a photograph. Passive liveness detection is less intrusive than active liveness detection, as it does not require the user to perform any specific actions.
Both methods have their own advantages and disadvantages. Active liveness detection can be more effective in detecting spoofing or impersonation attempts, but it may be more intrusive to the user. Passive liveness detection, on the other hand, is less intrusive to the user, but it may not be as effective in detecting spoofing or impersonation attempts. The choice of method will depend on the specific use case, the level of security required, and user convenience.
How is liveness detection done?
The steps involved in facial liveness detection are,
- The user is guided to blink or turn their head in randomised directions to evaluate their liveness.
- Liveness detection is done using special computer vision technology to check if the person is real.
- If the system confirms that the face in the image is a real, live human being, the person is authenticated. If the system cannot confirm the liveness, the person is not authenticated.
What are the benefits of liveness detection?
There are numerous benefits of using liveness detection to verify users, some of which include,
- Fraud prevention: Liveness detection can help prevent fraud by ensuring that a person being authenticated is not using a pre-recorded video or photograph of a legitimate user.
- Greater security: Liveness detection can improve the security of an authentication system by making it more difficult for an attacker to bypass the system using a fake or stolen identity.
- Enhance user experience: Liveness detection can enhance the user experience by providing a more seamless and secure authentication process.
- Compliance with regulations: Liveness detection can help organizations comply with regulations and industry standards that require multi-factor authentication, such as in the financial sector.
How can uqudo’s liveness detection safeguard your company?
uqudo’s face ID verification and liveness detection can be seamlessly integrated into your company’s digital onboarding platform. Our best-in-class biometric authentication system can make your onboarding process fast efficient and more secure.
To learn how you can leverage our authentication system into your identification journey, get in touch with our team.
