Talking about the three technology routes of RGB monocular liveness detection, IR binocular infrared liveness detection, and 3D Depth, there are differences in prevention capability and use cost.
RGB Monocular Liveness Detection
MiniAI Vision Open Platform RGB monocular liveness detection technology can be performed using a common RGB camera, by analyzing collected moiré patterns, imaging distortion, reflectivity, and other human face flaws to acquire the identification information needed for liveness detection, ensuring the accuracy of identification with multidimensional basis.
Feature: It uses a common monocular camera, so the cost is lower and it has a good defense against attacks such as screen imaging and paper photos.
IR Binocular Infrared Liveness Detection
MiniAI Vision Open Platform IR binocular infrared liveness detection, on the basis of the RGB monocular liveness detection algorithm capability, adds an infrared camera.
Since the infrared image filters out light of a specific wavelength, it inherently resists fake face attacks based on screen imaging. In fact, whether it’s visible light or infrared light, both are fundamentally electromagnetic waves. Imaging of an object is related to the reflective characteristics of its surface material. The reflective characteristics of a real human face and attack media such as paper, screens, and 3D masks are all different, so the imaging effects are also different.
This surface material difference is even more prominent in infrared wave reflection. When a face on the screen appears in front of an infrared camera, only a blank space is visible in the infrared imaging picture, and the face cannot be displayed. In other words, the infrared camera is a natural enemy of the photo revitalization attack mentioned earlier: as long as it needs to be imaged on the screen, the attack cannot work.
Feature: Due to hardware differences, the cost of infrared liveness detection is higher than RGB liveness detection. But at the same time, the defense against screen imaging and paper photo attacks is even better.
3D Depth Liveness Detection
3D Depth liveness detection uses structured light/TOF and other depth cameras, introducing the concept of “depth information”, which can obtain 3D data from the face area, and further analysis based on these data can easily distinguish fake face attacks from 2D media like paper photos and screens.
Feature: 3D Depth liveness detection has the best defense capability against attacks from screens, paper, and masks, but at the same time, the hardware cost is the highest.
In practical use, one usually needs to weigh the balance between cost and requirements, and choose the suitable liveness detection algorithm, so as to achieve maximum efficiency.
Although liveness detection technology can defend against the vast majority of counterfeit face attacks, when users use face-swapping apps, they should still stay vigilant. After all, in addition to applications like identity recognition and face verification, facial information can also be attached to illegal videos, leading to the risk of infringement of reputation rights and portrait rights.
Professionals can distinguish whether a video is real or not, but ordinary people are still easily fooled. In this age of “seeing is not necessarily believing,” ensuring personal information security requires technological progress like liveness detection technology and also needs users to increase their own prevention awareness.
