2D Face SDK

1. Deep adaptation to Android system features

System compatibility: Compatible with mainstream Android versions (7.0-14.0), compatible with customized systems from different manufacturers (HarmonyOS compatibility mode, MIUI, EMUI, etc.), solving fragmentation problems;

Hardware adaptation: Supports Android device cameras (front/rear), NPU/CPU/GPU heterogeneous computing, and can call hardware acceleration capabilities to improve algorithm efficiency;

Permission and Compliance: Adhere to Android privacy standards, access cameras/storage only after user authorization, and adapt to Android 10+partition storage, backend camera restrictions, and other rules;

Lightweight deployment: Supports APK sub packaging and on-demand loading, with a core algorithm library size controlled between 5-20MB to avoid occupying too much phone storage.

2. Characteristics of core algorithm capabilities

Mobile lightweighting: The algorithm model has been trimmed and quantified (such as INT8 quantization), and can run smoothly on low-end Android phones, with a single frame face detection time of ≤ 20ms;

Live detection capability: supports silent live (without user cooperation, judged by facial texture/micro movements), active live (blinking/shaking head/opening mouth), and prevents photo/video attacks;

Scenario robustness: Suitable for complex scenarios on mobile devices (backlight, low light, phone shake, different angles), even non face recognition can be stably detected with a false detection rate of less than 0.1%;

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

3. Development and integration characteristics

Quick integration: With a complete demo project (including layout files, permission requests, and calling examples), integration and debugging can be completed in 1-2 days;

Offline operation: The core functions (detection, recognition, live) do not require networking and only call the network when cloud comparison is needed, ensuring the availability of weak network scenarios.

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 level

Identity verification scenario: Addressing the issue of "face verification" for app login, payment, and real name authentication, replacing passwords/SMS verification codes to enhance security and convenience;

Life service scenario: Solve the problem of "face verification" for food delivery/express cabinet pickup, hotel check-in, and scenic spot ticket checking, without the need to carry physical identification, improving service efficiency;

Intelligent terminal scenario: solving the "face unlock/clock in" problem of Android smart door locks, tablet attendance machines, and self-service terminals, adapting to the portable characteristics of mobile devices;

Social entertainment scenario: Solve the problems of "facial effects, age/gender analysis, and expression recognition" in short video/beauty apps, and enrich the product interaction experience.

3. 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.