Add face recognition library in your embedded system application.
MicroFace SDK is a library aimed at system integrators willing to quickly add face recognition technology including face detection, tracking, analysis, liveness check and recognition capabilities to their embedded system. Depending on our customers’ needs, we can tailor our algorithms to specific targets.
Features included
Face Capture
Compatible with a wide range of cameras from leading manufacturers, ensuring versatile and comprehensive biometric data acquisition.
Face tracking
Provides precise and accurate face detection, capable of real-time processing and continuous monitoring with robust performance even under challenging conditions.
Facial Analysis
Offers multiple face analysis functionalities, including landmarks estimation, pose estimation, mask detection, and ICAO-compliant facial attributes determination.
Liveness Checks
Utilizes accurate passive and active liveness detection methods to protect against biometric fraud, such as presentation attacks using photos or videos.
Feature Extraction
Extracts facial features from detected faces, producing a compact face template for efficient face matching.
Face Recognition
Ensures accurate identification by matching detected faces against a database with ultra-fast performance in both one-to-one and one-to-many search modes.
Specifications
Optimized AI models.
Execution on Embedded Platforms.
The models we develop are optimized to reduce library size and improve execution speed. The templates generated by our extraction algorithms are among the smallest on the market (less than 148 bytes) and the matching algorithm also requires very low resources.
20 ms
Detection
30 ms
Extraction
0.01 ms
Matching
*Performances obtained on NXP i.MX 8M Plus.
Reliable technology proven in a world-renowned NIST evaluation.
id3 Technologies face recognition algorithm has proven excellent tradeoff between accuracy, speed and template size in the NIST ongoing Face Recognition Technology Evaluation (FRTE). The FRTE was initiated by the National Institute of Standards and Technologies (NIST) in February 2017. It is designed to measure the performance of automated face recognition technologies applied to a wide range of civil, law enforcement and homeland security applications including verification of visa images, de-duplication of passports, recognition across photojournalism images, and identification of child exploitation victims.
Developer documentation.
We provide comprehensive documentation and developer guides to facilitate a smooth implementation process of our Face sdk.
