Face Authentication Module
The Face Authentication module enables an automated selfie capture experience to identify if a user has an existing Identity in your Incode organization.
Face authentication provide an added level of security for organizations, applications, or even higher-level sections of application. For example, you could grant access to specific functionality within your application using face authentication. This would ensure the person requesting access has a valid, registered Incode Identity.
Add Face Authentication to Flows or Workflows
- In Dashboard, find Build & Verify in the left navigation. Select Flow Builder > Flows.
2. Click New.
3. On the Select Modules tab, find the Face Authentication Module and click Add.
4. You can click Details & Configurations to change specific settings for the module, like using 1:N, setting image quality, or setting a different number of allowed capture attempts. The Configuration Options section below describes all of these settings. The default settings work as designed, so no changes are needed.
5. Click Save Changes.
Configuration Options
After adding the Face Authentication module to your Incode Flow or Workflow, there are several settings you can configure based on your needs. Click the module in the Workflow to open the configuration panel, adjust the settings as needed, then click Save configurations.
Refer to the table below for details about each setting.
| Setting | Description |
|---|---|
| Mode | Determines the mode of comparison. Select one:
|
| Number of attempts | The maximum number of auto capture attempts Incode will make. As soon as there is a successful attempt, the module ends. Enter a number. Default value: 3 |
| Auto capture timout (secs) | The maximum number of seconds Incode will attempt to auto capture the selfie. After which time, manual capture is required. Enter a number of seconds. Default value: 25 |
| Show face capture tutorial | Controls whether the user sees a tutorial on how to capture the best possible selfie. Selected by default. |
| Stateless face match | When enabled, allows face authentication for users who have deleted their biometric data. The face match process uses the selfie submitted via API instead of a stored template. |
| Exact face match check | When enabled, compares the selfie captured during authentication against the selfie from the original onboarding session to detect the reuse of identical images. Face recognition scores above a fixed threshold are failed. This setting is disabled by default. |
| Face Match Threshold | Controls the severity on the model that does the face matching process. A high threshold is better for security, but a low threshold is better for conversions. Select one:
Note that this setting cannot be deselected as it is a crucial part of the authentication process. |
| Liveness Threshold | Controls the severity on the model that performs liveness checks. A high threshold is better for security, but a low threshold is better for conversions. There are three separate Liveness checks:
|
| Image Quality Threshold | Checks for the quality of the captured selfie. A high threshold is better for security, but a low threshold is better for conversions. Select one:
|
| Lenses validation | In the Face Attributes section. Controls whether the system checks if the user is wearing lenses or sunglasses in the selfie. Selected by default. |
| Mask validation | In the Face Attributes section. Controls whether the system checks if the user is wearing a mask in the selfie. Selected by default. |
| Hat validation | In the Face Attributes section. Controls whether the system checks if the user is wearing a hat in the selfie. Selected by default. |
| Closed eyes validation | In the Face Attributes section. Controls whether the system checks if the user's eyes are closed in the selfie. Selected by default. |
| Brightness validation | In the Face Attributes section. Controls whether the system checks for minimum necessary brightness of the image. Selected by default. |
{/ There is a mention of Deepsight configurations in the draft document, but no further info, and I don't see those in the demo env /}
Scoring
Every authentication attempt gets its own score.
When Liveness is OFF
- The numerical score represents the face recognition/match score.
- The face attributes have binary impact on the scoring - if any fail, the attempt fails
When Liveness is ON
- The numerical score represents combination face recognition/match score and Liveness score.
- The face attributes have binary impact on the scoring - if any fail, the attempt fails