Age estimation

Here users will simply look at the camera on a device and have their photo taken.

This option should be configured with a threshold higher than your age of interest.

Yoti details true positive rates for different ages in the Facial Age Estimation whitepaper which can be found here.

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.

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ParameterTypesDescription
allowedtrue / falseEnable the verification method to be available for the user to use.
thresholdInteger e.g. 30Age 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.
levelNONE 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.

Passive liveness

Passive liveness

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