Microsoft face authentication in Windows 10 is an enterprise-grade identity verification mechanism that's integrated into the Windows Biometric Framework (WBF) as a core Microsoft Windows component called Windows Hello. Windows Hello face authentication utilizes a camera specially configured for near infrared (IR) imaging to authenticate and unlock Windows devices as well as unlock your Microsoft Passport.

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The two primary scenarios for Windows Hello face authentication in Windows 10 are authentication to log on or unlock, and re-authentication to prove you are still there.

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When Microsoft talks about the accuracy of Windows Hello face authentication, there are three primary measures used: False Positives, True Positives, and False Negatives.

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Random errors results from using data that doesn’t match the population diversity that will actually be using the feature. For example, focusing on a small set of faces without glasses, beards, or unique facial features.

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Using IR also helps with spoofing because it helps prevent the most accessible attacks. For instance, IR doesn't display in photos because it's a different wavelength, and as you can see below, the images the images do not display in photos or on an LCD display.

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Enrollment is the step of generating a representation or set of representations of yourself (for example if you have glasses you may need to enroll with them and without them) and storing them in the system for future comparison. This collection of representations is called your enrollment profile. Microsoft never stores an actual image and your enrollment data is never sent to websites or applications for authentication.

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To ensure the algorithm has enough of your face in view to make an authentication decision, it ensures the user is facing towards the device +/- 15 degrees.

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The Windows Hello face recognition engine consists of four distinct steps that allow Windows to understand who is in front of the sensor:

Accounting for errors in measurement is important, so Microsoft categorizes them in two ways: bias errors (systematic errors) and random errors (sampling).

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Bias errors may occur as a result of not using data that is representative of the environments and the conditions in which the algorithm is used. This type of error can result from different environmental conditions (such as lighting, angle to sensor, distance, and so on) as well as hardware that is not representative if shipping devices.

Using the landmark locations as anchor points, the algorithm takes thousands of samples from different areas of the face to build a representation. The representation at its most basic form is a histogram representing the light and dark differences around specifics points. No image of the face is ever stored – it is only the representation.

After the release of face recognition with the first Kinect on Xbox 360, Microsoft learned that relying on ambient light to provide a consistent image provided a poor user experience. People live and work in a variety of environments, with an assortment of lighting conditions. Traditional color recognition systems rely on turning up the brightness, exposure, or other settings to create a useable image – all of which expose artifacts that impact the robustness of the system.

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Once there is a representation of the user in front of the sensor, it is compared to the enrolled users on the physical device. The representation must cross a machine-learned threshold before the algorithm will accept it as a correct match. If there are multiple users enrolled on the system, this threshold will increase accordingly to help ensure that security is not compromised.

It's strongly encouraged to run Windows Update constantly and make sure your system is updated with the latest security updates, including the updates released on July 13, 2021 to improve security when using Windows Hello camera described in CVE-2021-34466. In addition, if you want to disallow the use of external Hello camera completely, you can add an optional registry value in the following path. Registry path: HKEY_LOCAL_MACHINE\Software\Microsoft\Windows\CurrentVersion\Authentication\LogonUI\FaceLogon DWORD value: ShouldForbidExternalCameras Value: 1

In this first step, the algorithm detects the user’s face in the camera stream and then locates facial landmark points (also known as alignment points), which correspond to eyes, nose, mouth, and so on.