Face Recognition Guidelines

Whenever a face is detected on an IP camera or Doorkeeper kiosk, it is analysed by the Nirovision AI to assess its quality and provide a verdict on whether it matches a profile in the database.

The Nirovision AI has been designed designed and tested to meet accuracy standards within a specific context, which involves two main variables:

The quality of the images captured via IP cameras and Doorkeeper kiosks, so faces are detected as expected.
The quality of profiles, so matches occur as expected.

To address the first variable, we work with every customer and integration partner to ensure that cameras and kiosks are optimally placed and configured to the best possible settings. We also leverage heuristics in the Nirovision server and Doorkeeper to address scene-specific requirements and obstacles - and to improve the user experience. This is a crucial component behind the success of deployments, but not the whole story.

As for profiles, they can be created by Administrators, self-enrolled by users or synced by integrations. In all cases though, the quality of face(s) included in them directly impacts the quality of results achieved on your onsite.

1. Profiles that work

Generally, profiles do not need more than one, high quality face.

A high quality face is a sharp, bright, unoccluded (without sunglasses, masks, hats, scarves), front-facing, upright face. The best sources of high quality faces are the Doorkeeper kiosk, mobile selfies, and faces collected from a high-quality event.

Low-quality profile images include: blurry faces, zoomed-in faces, faces smaller than 100x100 pixels, distorted and stretched faces, partial and occluded faces, sideways faces.

A second, high quality quality face is needed in these scenarios:

If a person wears glasses, it's advisable to have a profile image with glasses and one without glasses, as sometimes the glass glare interferes with recognition.
If there are multiple kiosks and cameras connected onsite, which differ greatly in terms of lighting conditions, it's advisable to add one or two faces that illustrate significant lighting differences. The best way to diagnose this is to monitor if people struggle to be recognised on a specific camera using Camera and Similar filters of Activity.
Sometimes there is no other option but to use a lower quality profile image (e.g. collected from a Drivers' License), and wait for better quality faces to be collected by Doorkeepers or cameras.