3D Biometric Sensor Spoofing Detection via Structured Light
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current biometric systems relying on 2D fingerprint and palm print images lack effective methods to detect fake or spoofed biometric data, which can compromise security and identification processes.
Innovation Solution
The implementation of a 3D biometric image sensor using structured light illumination (SLI) with anti-spoofing techniques, including color spectrum analysis, reflectivity analysis, saturation analysis, and feature detection, to differentiate between genuine and fake biometric data by generating a 3D surface map and analyzing distortions in the SLI pattern.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 2D fingerprint and palm print images are used for biometric identification, then the system is simple and easy to implement, but the system cannot effectively detect fake or spoofed biometric data
Solution Approach 1:
The patent transitions from 2D biometric imaging to 3D structured light illumination (SLI) to capture depth information and surface characteristics of fingerprints and palm prints. This dimensional upgrade enables the system to detect spoofing attempts by analyzing the three-dimensional geometry and optical properties of the biometric sample, which cannot be achieved with conventional 2D imaging alone.
Solution Approach 2:
The system employs color spectrum analysis by illuminating the biometric sample with structured light at different wavelengths and analyzing the reflected light's color properties. Genuine skin exhibits specific color characteristics across different wavelengths, while fake biometric data shows abnormal color responses, enabling spoofing detection through spectral analysis.
Solution Approach 3:
The patent analyzes multiple optical parameters including reflectivity, saturation, and color spectrum to detect spoofing. By measuring these parameters across different wavelengths and comparing them against expected ranges for genuine skin, the system can identify fake biometric data that exhibits abnormal parameter values or combinations.
2Reliability
If 3D biometric imaging with structured light illumination is implemented, then spoofing detection capability is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent divides the biometric authentication process into distinct functional modules: structured light illumination generation, multi-wavelength image capture, color spectrum analysis, reflectivity analysis, saturation analysis, and spoofing detection. This segmentation allows each module to be optimized independently and facilitates parallel processing, reducing overall system complexity despite the advanced capabilities.
Solution Approach 2:
The image sensor system is designed to perform multiple functions simultaneously: capturing 3D depth information, analyzing color spectrum, measuring reflectivity, and detecting saturation. This multi-functionality is achieved through integrated processing circuits that can execute different analysis algorithms on the same captured images, reducing the need for separate dedicated hardware for each function.
3Measurement precision
If multiple analysis techniques (color spectrum, reflectivity, saturation) are used to detect fake biometric data, then detection accuracy is improved, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary analysis by first capturing images at multiple wavelengths and computing basic parameters (color spectrum, reflectivity, saturation) during the image acquisition phase. These pre-computed parameters are then used in subsequent spoofing detection algorithms, avoiding redundant calculations and reducing processing time during the actual authentication decision-making process.
Solution Approach 2:
The system implements a feedback mechanism where the results from color spectrum analysis, reflectivity analysis, and saturation analysis are integrated and compared against threshold values. Based on this feedback, the system can make rapid spoofing detection decisions or request additional verification, optimizing processing time by only performing full multi-parameter analysis when initially suspicious patterns are detected.
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
Effectively detects and differentiates fake biometric data by analyzing distortions in the SLI pattern, color spectrum, reflectivity, and saturation, enhancing the security and accuracy of biometric identification processes.
Implementation Method 1
A 3D biometric image sensor captures one or more images of a 3D biometric object, such as a fingerprint, palm print or other 3D biometric object
Implementation Method 2
including color spectrum analysis, reflectivity analysis, saturation analysis, and feature detection
Implementation Method 3
reflectivity analysis, saturation analysis, and feature detection
Data Source
AI summary
The system includes a 3D biometric image sensor and processing module operable to generate a 3D surface map of a biometric object, wherein the 3D surface map includes a plurality of 3D coordinates. The system performs one or more anti-spoofing techniques to determine a fake biometric.