Biometric Identification Using OCT Digital Codes
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Solution Overview
Problem
Current biometric identification methods, such as iris recognition, can be costly to implement and may be susceptible to falsification, while traditional methods like fingerprint recognition have limitations in accuracy and ease of modification.
Innovation Solution
The use of Optical Coherence Tomography (OCT) to generate unique digital codes from multidimensional images of human tissues like fingernails, irises, or corneas, which are then compared to reference codes for identification, allowing for non-invasive, high-resolution biometric classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iris recognition is used for biometric identification, then identification accuracy is improved, but implementation cost increases and susceptibility to cosmetic modification remains
Solution Approach 1:
The patent creates a digital code copy of the unique subsurface tissue structure that can be stored and compared without requiring repeated access to the original biological feature. This digital representation maintains identification accuracy while reducing the complexity of implementing full OCT imaging systems for every identification event.
Solution Approach 2:
The patent extracts the essential identification information from the complex OCT image data by generating a unique digital code that captures the distinctive subsurface tissue pattern. This extraction process separates the critical identification feature from the redundant imaging data, reducing processing and storage requirements.
2Device complexity
If traditional fingerprint recognition is used, then implementation cost is reduced, but identification accuracy and resistance to modification deteriorate
Solution Approach 1:
The patent transitions from two-dimensional fingerprint surface patterns to three-dimensional subsurface tissue structure imaging using OCT. This dimensional transition reveals unique internal tissue architectures that are invisible in traditional fingerprint scanning, thereby improving identification accuracy while maintaining relative simplicity through digital code generation.
Solution Approach 2:
The patent replaces mechanical contact-based fingerprint scanning with non-contact optical coherence tomography imaging. This substitution eliminates wear and contamination issues associated with mechanical systems while capturing deeper, more unique tissue characteristics for identification.
3Reliability
If subsurface tissue imaging is used, then resistance to cosmetic alteration is improved, but imaging complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the essential unique features from the complex three-dimensional subsurface tissue image by generating a condensed digital code. This extraction process maintains the anti-spoofing properties of subsurface imaging while dramatically reducing the complexity of data storage and comparison operations.
Solution Approach 2:
The patent transforms the complex spatial and intensity data from OCT imaging into a simplified digital code representation with different parameter characteristics. This parameter transformation preserves the unique identifying features and cosmetic alteration resistance while making the data more efficient for processing and comparison.
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 provides a fast, accurate, and difficult-to-modify biometric identification method that is resistant to cosmetic alterations, leveraging high-resolution imaging to generate unique codes for secure personal identification.
Implementation Method 1
Optical coherence tomography (OCT) is a noninvasive imaging technique that provides microscopic sectioning of biological samples. By measuring singly backscattered light as a function of depth, OCT fills a valuable niche in imaging of tissue structure, providing subsurface imaging with high spatial resolution (∼2.0-10.0 μm) in three dimensions and high sensitivity (>110 dB) in vivo with no contact needed between the probe and the tissue.
Implementation Method 2
The depth ranging capability of OCT is generally based on low-coherence interferometry, in which light from a broadband source is split between illuminating the sample of interest and a reference path. The interference pattern of light reflected or backscattered from the sample and light from the reference delay contains information about the location and scattering amplitude of the scatterers in the sample.
Implementation Method 3
The first, generally termed Spectral-domain or spectrometer-based OCT (SDOCT), uses a broadband light source and achieves spectral discrimination with a dispersive spectrometer in the detector arm.
Implementation Method 4
This approach involves acquiring the interferometric signal generated by mixing sample light with reference light at a fixed group delay in the wavelength or frequency domain and processing the Fourier transform of this spectral interferogram from a wavenumber to a spatial domain.
Implementation Method 5
The second, generally termed swept-source OCT (SSOCT) or optical frequency-domain imaging (OFDI), time-encodes wavenumber by rapidly tuning a narrowband source through a broad optical bandwidth.
Data Source
AI summary
Methods of providing a diagnosis using a digital code associated with an image are provided including collecting a multidimensional image, the multidimensional image having at least two dimensions; extracting a two dimensional subset of the multidimensional image; reducing the multidimensional image to a first code that is unique to the multidimensional image based on the extracted two dimensional image; comparing the first unique code associated with the subject to a library of reference codes, each of the reference codes in the library of reference codes being indicative of a class of objects; determining if the subject associated with the first unique code falls into at least one of the classes of objects associated with the reference codes based on a result of the comparison; and formulating a diagnostic decision based on the whether the first unique code associated with the subject falls into at least one of the classes associated with the reference code. Related systems and computer program products are also provided herein.