Multispectral Biometric Sensor for Tissue Authenticity Detection
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Solution Overview
Problem
Current biometric fingerprint sensors are prone to image-quality issues due to non-ideal conditions such as dry or wet skin, pressure variations, dirt, aging, and fine features, and are vulnerable to spoofing attempts using inanimate materials, leading to suboptimal performance and security concerns.
Innovation Solution
A multispectral biometric sensor system that evaluates the genuineness of a sample by illuminating it under distinct optical conditions, generating texture measures through spatial moving-window analysis, and determining authenticity using multidimensional scaling to distinguish between real biological tissue and spoofing materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optical fingerprint sensors are used, then the device complexity is low, but the reliability is poor due to vulnerability to spoofing and image-quality issues
Solution Approach 1:
The patent transitions from conventional 2D optical imaging to 3D optical coherence tomography (OCT) imaging. This dimensional enhancement enables depth-resolved cross-sectional visualization of fingerprint ridges and valleys, capturing subsurface structural information that distinguishes genuine fingers from spoofing materials. The 3D optical path difference measurements provide additional authentication dimensions beyond surface topology.
Solution Approach 2:
The system changes the measurement parameter from conventional 2D reflectance intensity to 3D optical path difference (OPD) measurements. By measuring the optical path length through tissue at different depths and comparing it to reference values, the system detects subtle structural variations in genuine versus spoofed fingerprints. This parameter transformation enables discrimination based on optical properties rather than just surface geometry.
2Measurement precision
If multispectral imaging with multiple illumination wavelengths is implemented, then the measurement precision for tissue authentication is improved, but the use of energy increases
Solution Approach 1:
The patent segments the broad spectral range into specific wavelength bands (e.g., 450-480nm, 520-550nm, 600-650nm) that target particular tissue chromophores. Each wavelength band provides complementary information about different tissue layers and components. This segmented approach achieves comprehensive tissue characterization while minimizing total energy consumption by avoiding unnecessary spectral coverage.
Solution Approach 2:
The system applies wavelength-specific illumination tailored to probe particular tissue depths and properties. Shorter wavelengths (blue) penetrate shallower and highlight epidermal features, while longer wavelengths (red) penetrate deeper into dermal layers. This localized spectral approach optimizes energy utilization by directing specific wavelengths to specific tissue regions of interest rather than uniformly illuminating all wavelengths across all tissue depths.
3Adaptability or versatility
If conventional single-wavelength optical sensors are used, then the ease of operation is high, but the ability to detect spoofing attempts is insufficient
Solution Approach 1:
The OCT-based system provides multiple authentication functions within a single platform: surface topology mapping, subsurface structural imaging, spectral analysis, and liveness detection. The same optical coherence tomography apparatus performs all these functions by processing different aspects of the optical backscatter signal, eliminating the need for separate sensors for each authentication modality and maintaining operational simplicity.
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 system effectively discriminates between real tissue and spoofing attempts, providing robust assurance of identity by leveraging differences in image contrast and spectral properties under various illumination conditions, thereby enhancing the reliability and security of biometric authentication.
Implementation Method 1
Light scattered from the sample is received
Data Source
AI summary
Methods are described of evaluating the genuineness of a sample presented for biometric evaluation. The sample is illuminated under distinct optical conditions. Light scattered from the sample is received. 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.