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Facial recognition technology is quickly implemented in fields such as finance and healthcare, which has in turn raised issues involving cyber security and information leakage, along with growing user expectations for improved app stability and security.

HMS Core ML Kit strives to help professionals from various industries work more efficiently, while also helping them detect and handle potential risks in advance. To this end, ML Kit has been working on improving its liveness detection capability. Using a training set with abundant samples, this capability has obtained an improved defense feature against presentation attacks, a higher pass rate when the recognized face is of a real person, and an SDK with heightened security. Recently, the algorithm of this capability has become the first on-device, RGB image-based liveness detection algorithm that has passed the comprehensive security assessments of China Financial Certification Authority (CFCA).

CFCA is a national authority of security authentication and a critical national infrastructure of financial information security, which is approved by the People's Bank of China (PBOC) and State Information Security Administration. After passing the algorithm assessment and software security assessment of CFCA, ML Kit's liveness detection has obtained the enhanced level certification of facial recognition in financial payment, a level that is established by the PBOC.

The trial regulations governing the secure implementation of facial recognition technology in offline payment were published by the PBOC in January 2019. Such regulations impose higher requirements on the performance indicators of liveness detection, as described in the table below. To obtain the enhanced level certification, a liveness detection algorithm must have an FAR less than 0.1% and an FRR less than 1%.

Level	Defense Against Presentation Attacks
Basic	When LDAFAR is 1%, LPFRR is less than or equal to 1%.
Enhanced	When LDAFAR is 0.1%, LPFRR is less than or equal to 1%.

Requirements on the performance indicators of a liveness detection algorithm

The liveness detection capability enables an app to have the facial recognition function. Specifically speaking, the capability requires a user to perform different actions, such as blinking, staring at the camera, opening their mouth, turning their head to the left or right, and nodding. The capability then uses technologies such as facial keypoint recognition and face tracking to compare two continuous frames, and determine whether the user is a real person in real time. Such a capability effectively defends against common attack types like photo printing, video replay, face masks, and image recapture. This helps distinguish frauds, protecting users.

Liveness detection from ML Kit can deliver a user-friendly interactive experience: During face detection, the capability provides prompts (indicating the lighting is too dark, the face is blurred, a mask or pair of sunglasses are blocking the view, and the face is too close to or far away from the camera) to help users complete face detection smoothly.

To strictly comply with the mentioned regulations, CFCA has come up with an extensive assessment system. The assessments that liveness detection has passed cover many items, including but not limited to data and communication security, interaction security, code and component security, software runtime security, and service function security.

Face samples used for assessing the capability are very diverse, originating from a range of different source types, such as images, videos, masks, head phantoms, and real people. The samples also take into consideration factors like the collection device type, sample textile, lighting, facial expression, and skin tone. The assessments cover more than 4000 scenarios, which echo the real ones in different fields. For example, remote registration of a financial service, hotel check-in, facial recognition-based access control, identity authentication on an e-commerce platform, live-streaming on a social media platform, and online examination.

In over 50,000 tests, ML Kit's liveness detection presented its certified defense capability that delivers protection against different attack types, such as people with a face mask, a face picture whose keypoint parts (like the eyes and mouth) are hollowed out, a frame or frames containing a face extracted from an HD video, a silicone facial mask, a 3D head phantom, and an adversarial example. The capability can accurately recognize and quickly intercept all the presentation attacks, regardless of whether the form is 2D or 3D.

Successfully passing the CFCA assessments is proof that the capability meets the standards of a national authority and of its compliance with security regulations.

The capability has so far been widely adopted by the internal core services of Huawei and the services (account security, identity verification, financial risk control, and more) of its external customers in various fields. Those are where liveness detection plays its role in ensuring user experience and information security in an all-round way.

Moving forward, ML Kit will remain committed to exploring cutting-edge AI technology that improves its liveness detection's security, pass rate, and usability and to better helping developers efficiently create tailored facial recognition apps.

Get more information at:

Home page of HMS Core ML Kit

Development Guide of HMS Core ML Kit