2D Face SDK

1. Adapt to Linux system features

Adopting Linux native development standards, controllable resource utilization, and adaptable to pure command-line environments without graphical interfaces;

Supports multi-threaded/multiprocessing calls, adapts to Linux's process management mechanism, and is suitable for high concurrency server deployment scenarios.

2. Characteristics of core algorithm capabilities

Lightweight: Compared to 3D facial SDK, it does not require a deep camera dependency and only needs regular 2D images to run. The algorithm has a small size and low computing power requirements, and can be deployed on low-end Linux embedded devices;

High precision: After training with massive 2D face data, the face detection rate is ≥ 99% and the recognition accuracy is ≥ 98% (1: N scene) in conventional scenes (front face, sufficient lighting);

Real time performance: Single frame face detection takes ≤ 10ms (Linux server) and ≤ 30ms (ARM embedded Linux), meeting the requirements of real-time video stream processing;

Robustness: Adapt to a certain degree of posture changes (± 30 ° side face), lighting changes (backlight/low light), slight occlusion (mask/glasses), and reduce environmental interference.

3. Development and integration characteristics

Provide native C/C++interfaces (mainstream Linux development language), with some SDK extensions supporting Java encapsulated interfaces;

1. Core algorithm layer: solves technical problems related to faces

This is the core value of the SDK, it encapsulates complex computer vision algorithms into simple API interfaces, eliminating the need for developers to build from scratch:

Facial detection: Quickly and accurately locates facial regions (including multiple faces) from any 2D image/video frame, solving the problem of "where faces are", while handling edge scenarios such as side faces, occlusion, dim lighting, and angle deviations.

Facial feature extraction and comparison: Converts detected faces into unique feature vectors (feature codes), and performs 1:1 (face verification, such as "face recognition login") and 1:N (face retrieval, such as "finding a person in a face database") comparison to solve the problem of "who this person is". The core is to ensure the accuracy and speed of comparison.

Facial attribute analysis: Identifies age, gender, facial expressions (smile/anger/cry),  mask/glasses wearing status, and facial key points (68/106 points such as eye corners, mouth corners,and nose bridge, etc.) to address the problem question of "what the characteristic attributes of the face are".

Live detection (2D version): Distinguishes real faces from fake faces such as photos,vidoes, and screen remakes, solving the basic security issue of "whether it is a real person performing face authentication" (Note: 2D liveness security is lower than 3D and mainly defend against static forgery).

2. Engineering landing layer: solvesthe adaptation and usability issues of Linux platform

Based on the characteristics of Linux system, the SDK solves the engineering difficulties of algorithm implementation:

Cross Linux Distribution Adaptation: Compatible with mainstream Linux systems such as Ubuntu, CentOS, and Debian, it handles compatibility issues with different kernel versions and dependent libraries (such as OpenCV and CUDA), so developers do not need to pay attention to underlying system differences.

Performance optimization: Optimize Linux's based multi-threading, memory management, and CPU/GPU (e.g.,NVIDIA graphics cards) scheduling to address the issues of slow algorithm operation and high resource consumption, such as supporting batch facial processing and  enabling lightweight operation on low-computing-power devices (e.g., Linux embedded devices).

Interface standardization: Provides unified APIs such as C/C++(the mainstream Linux development language), Python, etc.

Stability and compatibility: Deals with memory leaks, process crashes, camera/video stream reading exceptions, and other issues in Linux, providing exception handling mechanisms and log output to ensure the stable operation of the SDK in Linux production environments.

3. Business scenario layer: Solves the problems related to practical implementation needs

The 2D facial SDK adapted for Linux mainly serves scenarios such as server-side and embedded devices, addressing:

Server side batch processing: Perform batch, comparison, and feature storage of a large number of facial images/videos on Linux servers to meet the backend processing requirements of systems such as security, attendance, and identity verification.

Localized operation of embedded devices: Implement local face detection/comparison on Linux embedded devices (such as access control machines, facial recognition terminals, and smart cameras) without relying on the cloud, addressing the needs of scenarios with poor network conditions and sensitive privacy.

Cross-platform integration: Seamlessly-integrate with other systems under Linux, such as security monitoring systems and attendance management systems, provide standardized calling methods and reduce the development and integration costs of business systems.