Liveness Detection Using Photodiode Depth Data
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Solution Overview
Problem
Current face authentication systems are vulnerable to 2D spoofing attacks, such as using photographs or screens, due to their reliance on two-dimensional image data, which lacks robustness against such threats.
Innovation Solution
A liveness test method and apparatus that utilize both color images and photodiode (PD) images, incorporating neural networks to calculate a liveness score based on weighted outputs from preprocessing these images, thereby enhancing the accuracy of distinguishing between genuine and fake faces by incorporating three-dimensional depth information from PD images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If only color images are used for face authentication, then the system is simple to implement, but it becomes vulnerable to 2D spoofing attacks
Solution Approach 1:
The patent transitions from 2D color image data to 3D depth information by incorporating photodiode images that capture light intensity variations. This dimensional change enables the system to detect spatial depth and surface geometry, making 2D spoofing attacks ineffective while maintaining sensor integration.
Solution Approach 2:
The patent combines multiple types of image data (color images and photodiode images) into a composite authentication system. By fusing 2D color information with 3D depth information from photodiodes, the system creates a multi-modal authentication approach that resists spoofing while leveraging the strengths of each data type.
2Measurement precision
If photodiode images are incorporated to provide 3D depth information, then spoofing detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent divides the authentication process into separate processing streams: one for color image analysis and another for photodiode image analysis. Each stream is processed independently through dedicated neural network branches, which are then combined. This segmentation allows optimized processing for each data type while maintaining overall system accuracy.
Solution Approach 2:
The patent merges the outputs of separate neural network processors that handle color images and photodiode images respectively. By combining these processed results in a unified decision-making layer, the system achieves high liveness detection accuracy while managing computational complexity through modular architecture.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The approach effectively prevents false acceptance caused by spoofing technologies and improves the accuracy of biometric authentication by differentiating between animate and inanimate objects, providing robustness against 2D spoofing attacks.
Implementation Method 1
receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs
Data Source
AI summary
Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.