Strengthening Safe User Verification With Video Liveness Detection
This technology confirms that the information or data entered into the system was provided by an actual, living individual. Fake photos, body parts, and AI-generated films can be used to fool the system. Liveness detection looks for clues that a picture or video isn’t authentic. Skin texture, blinking, smiling, blood flow, and heart rate are all assessed. Immediately identifying the spoof, aids in preventing fraud. Additionally, it increases the efficacy, security, and accuracy of biometric systems and fosters user confidence in them. Numerous locations, including banks, airports, biometric systems, and many more, use this verification technology.
Video Liveness Detection
Video liveness is a method that verifies that the person is physically present in the video without using fake pictures, videos, and masks. This approach evaluates the biometric sample by looking at the angle of the faces, the robotics movement in a video, the reflection, or the texture of the skin. In order to determine whether the image is of a live or actual person, it is examined from several perspectives to observe how light reflects. The deepfake can be easily identified with this 3D liveness check in a video.
Difference Between the Types
There are two main types of liveness detection namely active and passive. This type of verification is possible in videos and is rare for evaluating pictures.
| Active Liveness | Passive Liveness | Video Liveness |
| It requires direct user interaction. | It ensures liveness without direct interaction with the user. | Real-time video is required to verify liveness. |
| For example, uses blinking, and smiling to check liveness. | It evaluates facial expressions, depth, and lighting | Interaction relies on implementation. |
| AI evaluates specific user interactions. | AI identifies micro-movements and reflections | AI interprets continuous video frames. |
| It is slow due to user interaction. | It is a quick process because it doesn’t require direct user interaction. | It works at a medium speed because it evaluates multiple frames. |
| It is commonly used in banking, eKYC, and account verification. | Used for making mobile payments, face unlocking, and security access control. | It is a video-based process that is beneficial for remote onboarding. |
Benefits of Liveness Detection
Financial institutions can improve security and user experience by implementing facial liveness detection for secure transactions. Among the main benefits are:
- It protects against fraud using pictures, videos, and deepfakes by evaluating natural movements like blinking, smiling, and others.
- It makes sure that only the rightful owner and user can get access which is essential for banking, fintech, and other high-security applications.
- It ensures that the real person is present leading to safe and secure transactions and secures account opening procedures.
- It promotes convenient authentication by eliminating the need for complex passwords and security interrogations.
- It helps businesses comply with the rules of KYC, AML, and GDPR by ensuring stringency in identity verification processes.
- It is used in several industries like banking, retail, marketing, government organizations, and others to promote secure verification.
Challenges Faced in Liveness Detection
This technology has gone too far in detecting spoofs. Nevertheless, a number of challenges could affect its efficacy. One environmental factor that might have a detrimental effect on the verification process is inadequate lighting. Because of the background noise, it could be difficult to verify the identification. Furthermore, sensors are not particularly good at identifying spoofs. This could lead to a high number of false rejections. Furthermore, certain biometric systems are slow and require two or more attempts to identify the fake. As a result, users could get annoyed and waste time.
Additionally, certain users might be unable to complete face-liveness test behaviors, including blinking or smiling, due to constraints. This can irritate those who have limitations. Additionally, not all users are proficient with this technology. Actions like smiling and making facial expressions might cause discomfort and distress for some individuals. Such challenges are unbeatable with the implementation of contemporary systems that operate efficiently and equitably for everybody.
Conclusion
It is a crucial process that is used to authenticate the physical presence of the user. This detection process evaluates facial features, robotics movements, blinking, smiling, and muscle contraction in the video to ensure presence. Moreover, the video selfie with liveness detection is beneficial for several industries including banking, healthcare, fintech, and e-commerce. As the technology advances, it will likely enhance more and offer robust protection against fraudulent attempts.
