Liveness Detection – A Process of Preventing Presentation Attacks

3 minutes read

It is a technology that verifies that the data or information provided to the system came from a real and alive person. The system can be tricked using fake images, body parts, and AI-generated videos. Liveness detection examines the signs that indicate that the video or photo is not real. The evaluation includes skin texture, blinking, smiling, blood flow, and heart rate. It helps to avert fraud by detecting the spoof instantly. Besides, it also helps to boost security, accuracy, and efficacy and builds trust in biometric systems. This verification process is being utilized in several places such as banks, airports, biometric systems, and many more.

Implementation of Liveness Detection Technology

This advanced technology is being used in numerous places to enhance security. The system is well-trained in detecting fraud and has been adapted by many institutions to boost security levels.

Smartphones and devices have fingerprint IDs and face recognition to unlock them but liveness detection ensures that the picture or video and fingerprint must be from a real person.
The technology ensures that the transactions made in banks are from a real and authorized person. It helps make secure transactions.
In places like airports and offices, this technology makes sure that access is granted only to legitimate people.
This process is also helpful in identifying people who are working remotely. People can apply for loans and open their accounts securely by using this detection method.
It is also being used in several healthcare centers to verify patients and make sure that the right patient is getting the right treatment.
At border control, the application of this technology is highly appreciated where it is necessary to get access after verification.

Types of Face Liveness Detection

The two main types of liveness detection are active liveness and passive liveness. A process that involves the user’s interaction and the user will be asked to make certain movements such as blinking, smiling, and others is termed as active liveness. Also, the person in a video will be asked to make movements like moving the hands, and body and change the expressions. This will ensure that the person in a video is alive and not a deepfake.

Moreover, passive liveness is a process that does not involve user interaction. In this method, the biometric sample is evaluated based on skin texture, the robotics movement in a video, or the angle of the images. The particular image is analyzed by viewing it from different angles to see how light reflects and ensure that the image is from a live or real person. This 3d liveness check can effortlessly detect the deepfake detection of a person.

Challenges Faced by Liveness Detection

This technology has gone too far to detect spoofs. But still, several challenges can affect its efficacy. Certain environmental features like improper lighting might make the verification process suffer. Sometimes, the background noises can make it difficult to verify the identity. Besides, sensors are not that clever to detect the spoof. This may result in a high rate of false rejections. Also, some of the biometric systems are slow and detect the spoof after two or more attempts. It can irritate the users and result in the wastage of time.

On top of that, there are some users with disabilities and they might not be able to perform face-liveness test actions like blinking, smiling, and others. This can be frustrating for those disabled users. besides, not all users are aware of the use of this technology. Actions like smiling and giving expressions can cause anxiety and unease to particular users. Such challenges can be resolved by implementing up-to-date systems that can work efficiently and equally for everyone.

Conclusion

As discussed above, the liveness detection technology is being used for maintaining high security and to avert fraud in various organizations like banks, airports, online platforms, gaming, offices, and most importantly in other financial institutions. Fraudsters can be easily caught by the implementation of this technology. The incorporation of liveness detection technology in biometric systems will enhance the accuracy and efficiency of security management systems. Biometric liveness detection can add an extra layer of security. Besides, 3D liveness detection is more accurate and detects the spoof proficiently.