1. What is Face Capture?

Face Capture uses face detection algorithms to capture selfie images and detect whether a person is real. It is applicable for a variety of scenarios such as remote identity authentication, etc; which effectively improves overall business efficiency and enhances user experience.

Face Capture collects users’ selfie images, and asks users to cooperate by blinking/performing other actions to complete their face capture and liveness detection. During this process, Face Capture detects whether the user's face is genuine; and if prints, screen remakes and masks etc, as well as other live attack methods have been used. This helps to ensure the authenticity of your users and their profile information.

The UI for Face Capture is shown in the following figure:

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Figure 1: Face Capture UI

1.1 Features

Face capture

Face Capture captures users’ live selfie images. The user cooperates by lifting the phone and facing the screen, then follows prompts such as blink/performing other actions so that a selfie image of their face can be captured and collected through the front camera. The algorithm will automatically determine whether the user’s face is genuine and whether the quality of the selfie image is acceptable.

Liveness detection

By capturing and analyzing face images, ZOLOZ determines whether it has collected a real face, or a photo or video. ZOLOZ provides a variety of image algorithm capabilities for liveness detection, and it can help you to identify and defend against presentational attacks such as 2D images, screen remakes, 3D masks, and more. It can also combine multi-frame image and anti-spoofing algorithms to identify injection attacks, before finally completing the face capture process.

Deepfake detection

Face Capture has deepfake detection capabilities. Through a comprehensive end-to-cloud AIGC detection solution, combining multimodal algorithms, terminal security detection capabilities, and dynamic risk control strategies, it can identify AIGC risks in identity verification from multiple dimensions. We refer to this capability as Deeper. For more information on Deeper capabilities, see What is Deeper?

1.2 Accessibility modes

Face Capture provides the following two accessibility modes. For more details about the accessibility modes, please refer to Understand integration modes.

Native App SDK Mode: Provides Native SDK and server-side API, supporting Android and iOS system apps.
Web SDK (H5) Mode: Provides Web SDK and server-side API, supporting the use of mobile browsers on Android and iOS systems.

1.3 Use Flow

The use flow of Face Capture is shown below:

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Figure 2: Face Capture use flow illustration

Capture a live faceUsers will need to raise their phone and face the screen. They will have to follow the prompts given and complete certain actions i.e. blinking/other actions, as well as take a selfie using the front camera.The algorithm will automatically determine whether the face captured in the selfie is human, and whether the quality of the face capture is acceptable.
- When an eligible selfie image is detected, the algorithm will automatically capture and upload it. This capture process does not require the user to tap on the screen.
- If the algorithm detects that the quality of the selfie is inadequate, it will guide the user to adjust their phone with corresponding prompts, such as if they should move closer or further, if their face needs to be better lit, etc.To verify that it is indeed a genuine human face in front of the camera and not a photo, the algorithm will ask the user to perform an action such as blinking. The capture process will only be successful when the prompted action has been performed.

Liveness detectionAfter the selfie image has been successfully captured, the image will be uploaded to ZOLOZ’s server for further face liveness detection.
Face capture resultsWhen the face liveness detection is completed, the results for the face capture images and liveness detection will be released in ZOLOZ’s server.

1.4 Face Capture results

Field name	Meaning	Description
faceCaptureResult	Total Face Capture Results	The total face capture result will be returned in the form of Success, Failure, VoidTimeout, InProcess. Success: The user has passed the face quality check and liveness detection Failure: A high risk has been detected and rejecting the user is recommended i.e. the user’s face quality of face liveness detection is poor VoidTimeout, InProcess: The face capture result has not yet been obtained e.g. the process has timed out or is still in progress.
ExtInfo.faceAttack	Liveness detection test results	The liveness detection result will be returned as either true/false. true: The liveness detection has passed. false: The liveness detection has failed. For example, this may have been a presentation attack.
qualityPassed	Total face quality results	The face quality result will be returned as either true/false. Face quality detection supports multiple algorithms for detection, including face quality score, mask detection, and occlusion detection. Face quality score (whether the face is clear, complete, etc) is detected by default, and you can turn on more detection algorithms according to your actual business needs. true: The quality detection has passed. false: The quality detection has failed. For example, the image may have been blurred, obscured by something, etc.
quality	Face quality score	The total quality result of the selfie image. If the quality score ≥ the quality score threshold set by ZOLOZ, the image quality meets the requirements. If the quality score < quality score threshold set by ZOLOZ, the image quality does not meet the requirements, which results in the total result of the face session failing.

2. Product advantages

2.1 Fully automated process

With the introduction of ZOLOZ’s deep learning and risk control models, the entire process will be completed automatically and there is no need to wait for immediate results. The average application time takes 1 minute and Face Capture results are available within 10 seconds; providing your users with a great user experience while helping you save on labor costs.

2.2 Cross-platform support

ZOLOZ support iOS, Android, HarmonyOS, Web, API access, App support, WeChat H5, mobile browsers and other types of applications to meet all of your business needs.

2.3 Effective fraud risk protection

ZOLOZ has passed the ISO30107-3 (iBeta1/2) certification, accrediting ZOLOZ's face liveness detection capabilities as being on par with international standards.
ZOLOZ provides a variety of liveness detection solutions that can be either integrated alone, or combined with multiple solutions, such as random action combinations, multi-frame photo recognition, photo quality verification, etc; to effectively defend against common fraudulent behaviors such as photo prints, screen remakes, etc.

3. Common scenarios

3.1 Remote identity authentication

In industries such as finance, insurance, life services, healthcare education, transportation and more; for online account openings, account creation, etc; mobile devices are used to collect face profile information to determine if the person is genuine. This helps you to ensure transaction and account security, as well as enhance the efficiency of remote processes for your business.

3.2 Face capture and detection

When it comes to banking and government affairs, mobile devices are used to collect users’ selfie images for retention. To check if captured images meet the desired security requirements, you can combine the capabilities of multiple face and mask detection in the overall face capture solution provided by ZOLOZ.