Facial Liveness Detection with Feature Point Transforms for Edge Devices
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Solution Overview
Problem
Edge devices with facial recognition capabilities often lack sufficient computing and memory resources to support liveness detection functions, leading to potential spoofing vulnerabilities.
Innovation Solution
A liveness detection method and system that utilize feature point processes and transform functions to determine whether an image is spoofing, reducing resource requirements through simple operations like addition, subtraction, multiplication, and division.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a liveness detection function is implemented on an edge device, then spoofing prevention capability is improved, but computing resources and memory resources are exceeded
Solution Approach 1:
The patent extracts only the essential feature points needed for liveness detection from the full image, rather than processing the entire image. By selecting key feature points (such as facial landmarks) and using them for transform function calculation, the system reduces the data volume and computational load while maintaining detection accuracy, thus solving the resource constraint problem on edge devices
Solution Approach 2:
The patent changes the parameter representation from full image data to discrete feature point coordinates. By transforming the liveness detection problem from image-level processing to feature point-level processing, the computational complexity is significantly reduced, enabling implementation on resource-constrained edge devices while preserving spoofing detection capability
Data Source
AI summary
A liveness detection method includes: obtaining multiple first feature points and multiple second feature points according to a first image, a second image, and a feature point process, wherein the first feature points include two first fixed feature points and at least one first variable feature point, and the second feature points include two second fixed feature points and at least one second variable point; obtaining a first transform function according to the first fixed feature points and the second fixed feature points; obtaining at least one check feature point according to the first transform function and the first variable feature point(s); and determining whether the second image is a spoofing image according to the check feature point(s) and the second variable point(s).