Multispectral Biometric Sensor for Spoof Detection
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Solution Overview
Problem
Current biometric fingerprint sensors face challenges such as poor image quality due to non-ideal skin conditions, susceptibility to spoof samples, and replay attacks, which affect the accuracy and security of biometric measurements.
Innovation Solution
The use of multispectral imaging systems that illuminate the sample under various optical conditions, generate texture measures, and apply multidimensional scaling to determine the authenticity of biological tissue, combining spatial and spectral information to enhance biometric identification and analyte estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional optical fingerprint readers use a single quasimonochromatic beam for TIR-based measurement, then the device structure is simple, but image quality deteriorates under non-ideal skin conditions (dry skin, wet valleys, contamination)
Solution Approach 1:
The patent transitions from single-wavelength optical measurement to multispectral imaging by adding the spectral dimension. Multiple wavelengths (e.g., 450nm, 550nm, 650nm, 780nm) are used to illuminate the fingerprint, enabling differentiation of skin conditions and spoofing attempts based on their distinct spectral signatures. This dimensional expansion resolves the contradiction by improving image quality through spectral information without requiring complex mechanical modifications to the basic sensor structure.
Solution Approach 2:
The patent changes the illumination parameter from single-wavelength to multi-wavelength light sources. By varying the spectral parameter and analyzing reflectance characteristics at different wavelengths, the system can distinguish between genuine fingerprints and spoofs, as well as compensate for non-ideal skin conditions. This parameter change enables robust authentication while maintaining a relatively simple device architecture.
2Reliability
If optical fingerprint readers rely on surface skin characteristics, then the measurement process is simple, but susceptibility to spoof samples increases
Solution Approach 1:
The patent adds spectral dimension analysis to detect spoofing attempts. Genuine skin exhibits specific spectral absorption and reflection patterns across multiple wavelengths that synthetic materials (gelatin, epoxy, latex) cannot replicate. By analyzing reflectance at wavelengths such as 450nm, 550nm, 650nm, and 780nm, the system can identify spoof samples with high reliability, resolving the contradiction between simple measurement and spoof detection capability.
Solution Approach 2:
The system changes from measuring only spatial fingerprint patterns to measuring spectral reflectance parameters at multiple wavelengths. This parameter expansion enables the detection of subtle differences between genuine and spoofed fingerprints, as biological tissue has unique optical properties across the spectrum that artificial materials lack. The increased measurement complexity is offset by the significant improvement in authentication security.
3Measurement precision
If multispectral imaging systems use multiple wavelengths to illuminate the sample, then measurement precision improves, but energy consumption increases
Solution Approach 1:
The patent employs periodic or sequential illumination at different wavelengths rather than simultaneous multi-wavelength illumination. The light sources are activated in sequence (e.g., blue LED at 450nm, green at 550nm, red at 650nm, NIR at 780nm), with each wavelength illuminating the sample for a brief period. This periodic action reduces total energy consumption compared to continuous multi-wavelength illumination while still capturing the necessary spectral information for accurate biometric measurement.
Solution Approach 2:
The system uses a limited set of strategically selected wavelengths (450nm, 550nm, 650nm, 780nm) rather than continuous spectral scanning. This partial action approach focuses energy on specific wavelengths that provide the most discriminative information for fingerprint authentication and spoof detection, reducing overall energy consumption while maintaining high measurement precision.
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
This approach improves the accuracy and security of biometric measurements by distinguishing genuine biological tissue from spoof samples and providing robust authentication, while also enabling simultaneous analyte estimation, thus addressing the limitations of existing technologies.
Implementation Method 1
A detection subsystem is disposed to receive light scattered from the purported skin site
Implementation Method 2
optical arrangements used to detect this variation of the optical interface in both bright-field and dark-field optical arrangements
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
Methods are described of evaluating the genuineness of a sample presented for biometric evaluation (figure 14). The sample is illuminated under distinct optical conditions (1404). Light scattered from the sample is received (1408). Multiple images are formed, each image being formed from the received light for one of the optical conditions. A set of texture measures is generated, each texture measure being generated from one of the images. It is determined whether the generated texture measures is consistent with the sample being authentic unconcealed biological tissue (1420).