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: solving technical problems related to faces

This is the core value of SDK, which encapsulates complex computer vision algorithms into simple API interfaces, avoiding developers from developing from scratch:

Facial detection: Quickly and accurately locate facial regions (including multiple faces) from any 2D image/video frame, solving the problem of "where there is a face", while handling edge scenes such as side faces, occlusion, dim lighting, and angle deviation.

Facial feature extraction and comparison: Convert the detected face into a unique feature vector (feature code), and achieve 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 the comparison.

Facial attribute analysis: Identify age, gender, facial expressions (smile/anger/cry), whether wearing masks/glasses, and facial key points (68/106 points such as corners of the eyes, corners of the mouth, nose bridge, etc.) to solve the problem of "what are the characteristic attributes of the face".

Live detection (2D version): Distinguish between real faces and photo/video/screen remakes of fake faces, and solve the basic security issue of "whether it is a real person brushing the face" (note: 2D live security is lower than 3D, mainly to prevent static forgery).

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

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

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

Performance optimization: Optimize Linux's multi threading, memory management, and CPU/GPU (such as NVIDIA graphics cards) scheduling to solve the problems of slow algorithm running and high resource consumption, such as supporting batch face processing and lightweight operation of low computing power devices (such as Linux embedded devices).

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

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

3. Business scenario layer: solving practical implementation needs problems

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

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

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, solving the needs of poor network and privacy sensitive scenarios.

Cross platform integration: Seamless integration with other systems under Linux, such as security monitoring systems and attendance management systems, providing standardized calling methods and reducing the development and integration costs of business systems.