Here users will simply look at the camera on a device and have their photo taken.
The image is analysed by an algorithm that has been trained to determine age by analysing facial features. A robust facial age estimation process should also include liveness detection technology to ensure it’s a real person in front of the camera.
Good for:
People without ID documents
Global coverage
Low friction
If you wish to enable the Age estimation service as an option to perform an age verification service.
Parameter | Types | Description |
|---|---|---|
allowed | true / false | Enable the verification method to be available for the user to use. |
threshold | Integer e.g. 30 | Age threshold for under/over age limits. We recommend for this threshold to be more than the age you want to set as your barrier to entry. |
level | NONE PASSIVE | The level of anti-spoofing for each age verification method. PASSIVE enables a passive liveness test for age estimation. |
For extra security, you can also request a liveness test (level). This is to make sure it’s a real person behind the camera, and not a 2D image, mask or bot. The technology works by processing the image(s) through a sequence of deep neural networks. Each of these examine a different element of the image to look for clues that it might not be a real person.
Liveness explained
Passive liveness
Passive liveness looks at the texture, depth and edges of a person’s face and their surroundings for signs of spoofing and requires no movement from the user.
