2D Face SDK

1. Deep adaptation to Android system features

System compatibility: Compatible with mainstream Android versions (7.0-14.0), and customized systems from various manufacturers including HarmonyOS compatibility mode, MIUI, EMUI, etc., effectively addressing fragmentation issues;

Hardware adaptation: Supports front and rear cameras on Android devices, as well as NPU/CPU/GPU heterogeneous computing. Hardware acceleration can be utilized to enhance algorithm efficiency;

Permission and Compliance: Adheres to Android privacy standards, accesses cameras and storage only after user authorization, and complies with Android 10+ rules including scoped storage and background camera restrictions;

Lightweight deployment: Supports APK splitting and on-demand loading, with the core algorithm library size controlled between 5-20MB to avoid excessive storage usage on mobile devices.

2. Characteristics of core algorithm capabilities

Mobile lightweighting: The algorithm model has been pruned and quantized (e.g.,INT8 quantization), enabling smooth operation on low-end Android phones, with single frame face detection time ≤ 20ms;

Live detection capability: Supports silent liveness (no user cooperation, identified via facial texture and micro-movements) and action liveness (eye blinking, head shaking, mouth opening ), and prevents photo/video spoofing attacks;

Scenario robustness: Adapts to complex scenarios on mobile devices (backlight, low light, phone shaking, various angles), enabling stable detection even for non-frontal faces, with a false detection rate＜0.1%;

Strong real-time performance: Supports real-time camera stream processing, enabling "second-level" face unlocking and identity verification, meeting the interactive experience requirements of mobile users.

1. Technical development level

Solving the problem of "difficulty in developing mobile facial algorithms": without the need to master complex technologies such as mobile model optimization, hardware adaptation, Android permission management, etc., core functions can be achieved through API calls, reducing the development threshold;

Solving the problem of "difficult adaptation to Android fragmentation": The SDK has been pre adapted to Android devices of different brands, versions, and hardware to avoid developers debugging compatibility issues one by one;

Addressing the issue of balancing algorithm performance and power consumption: optimizing computing power scheduling based on the battery characteristics of Android devices, reducing power consumption while ensuring recognition speed, and avoiding phone overheating.

2. Business application aspects

Identity verification scenarios: Addressing facial verification needs for app login, payment, and real-name authentication, replacing passwords and SMS verification codes to enhance security and convenience;

Life service scenarios: Solve face verification for food delivery/express locker pickup, hotel check-in, and scenic spot ticket checking, with no need to carry physical ID, improving service efficiency;

Intelligent terminal scenarioS: Solving the "face unlock/clock-in" issues for Android smart door locks, tablet attendance machines, and self-service terminals, adapting to the portable nature of mobile devices;

Social entertainment scenarios: Solving issues of "facial effects, age/gender analysis and expression recognition" in short video and beauty apps, enriching the product interaction experience.

3. Security and experience aspects

Addressing the issue of "facial forgery attacks": using liveness detection technology to prevent fraudulent methods such as photos, videos, and 3D masks, and ensuring the security of identity verification;

Addressing the issue of "weak/no network usage on mobile devices": Offline operation capability ensures that facial functions still work normally in weak or no-network scenarios such as subways and mountainous areas;

4. Safety and experience aspects

Addressing the issue of "facial forgery attacks": using liveness detection technology to prevent fraudulent methods such as photos, videos, and 3D masks, ensuring the security of identity verification;

Addressing the issue of "weak/no network usage on mobile devices": The offline operation capability ensures that the facial function can still be used normally in weak network scenarios such as subways and mountainous areas;

Addressing the issue of poor user experience: optimizing mobile interaction logic (such as guiding users to face the camera or indicating insufficient lighting), reducing verification failure rates, and improving user experience.