Adaptive Depth Selection for 3D OCT Fingerprint Extraction
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Solution Overview
Problem
Existing techniques for deriving dermal fingerprint images from OCT data face challenges due to individual variations in epidermal thickness and potential distortions in the depth direction of skin interfaces, leading to unclear or blurred images.
Innovation Solution
A processing apparatus and method that calculate the depth dependence of striped pattern sharpness in multiple regions of 3D luminance data, adjust the depth to correct deviations, and select the depth with the maximum striped pattern sharpness for extracting a clear 2D fingerprint image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed depth range is used for averaging tomographic luminance images, then processing is simplified, but individual variations in epidermal thickness are not accounted for, resulting in unclear dermal fingerprint images
Solution Approach 1:
The patent applies dynamics by making the depth range adaptive rather than fixed. The system dynamically adjusts the depth range for averaging based on individual epidermal thickness variations, allowing the processing parameters to change according to the specific subject being measured. This resolves the contradiction by maintaining processing simplicity while improving image clarity through adaptive depth selection.
Solution Approach 2:
The patent changes the parameter of depth range from a fixed value to a variable that adapts to individual characteristics. By modifying the depth parameter based on detected epidermal thickness, the system achieves both processing efficiency and high image quality, resolving the technical contradiction between simplicity and precision.
2Adaptability or versatility
If the depth range is expanded to cover thicker epidermises, then more individuals can be accommodated, but the averaging range may include regions beyond the dermal fingerprint, causing image blurring
Solution Approach 1:
The patent applies local quality by adjusting the depth range locally for each individual based on their specific epidermal thickness. Rather than using a uniform depth range for all subjects, the system tailors the averaging depth to match the local characteristics of each person's skin structure, thereby maintaining image sharpness while accommodating individual variations.
Solution Approach 2:
The system dynamically adjusts the depth parameter based on detected epidermal characteristics, making the processing adaptive to individual variations. This dynamic adjustment ensures that the averaging range always corresponds to the actual dermal fingerprint location, preventing blur while covering diverse individual cases.
3Measurement precision
If depth is determined for each pixel independently, then local variations are captured, but processing time increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the fingerprint image into multiple regions and determining depth characteristics for each region independently. This allows local variations in epidermal thickness to be captured while reducing the overall processing burden compared to pixel-by-pixel analysis, thereby balancing depth accuracy with processing speed.
Solution Approach 2:
The system merges the depth information from multiple regions to generate the final depth map. By combining region-level depth determinations rather than processing each pixel independently, the patent achieves both local accuracy and improved processing efficiency, resolving the contradiction between precision and productivity.
4Productivity
If a single depth image is extracted without regional adjustment, then processing is fast, but interfaces distorted in the depth direction cause local blurring
Solution Approach 1:
The patent segments the fingerprint image into multiple regions and processes each region separately with its own optimized depth parameter. This segmentation approach maintains processing speed while eliminating local blurring caused by depth direction distortions, as each region can be extracted at its optimal depth without being constrained by a single global depth setting.
Solution Approach 2:
The system applies local quality by using different depth parameters for different regions of the fingerprint image. This allows each region to be extracted with optimal sharpness according to its local characteristics, preventing blur from depth distortions while maintaining overall processing efficiency through regional rather than pixel-level processing.
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 enables the extraction of clear and accurate 2D fingerprint images from 3D OCT data, adaptively addressing individual variations in epidermal thickness and minimizing noise and distortion, while also improving processing speed.
Implementation Method 1
a tomographic image of a part of an object to be measured near the surface thereof is taken by using interference between scattered light that is emitted from the inside of the object to be measured when a light beam is applied to the object to be measured (hereinafter referred to as 'back-scattered light') and reference light
Data Source
AI summary
Provided is a processing apparatus capable of obtaining a 2D image from 3D tomographic images, extracting an image for accurate authentication, and extracting an image at a high speed. A processing apparatus includes: means for calculating, from three-dimensional luminance data indicating an authentication target, depth dependence of striped pattern sharpness in a plurality of regions on a plane perpendicular to a depth direction of the target; means for calculating a depth at which the striped pattern sharpness is the greatest in the depth dependence of striped pattern sharpness; rough adjustment means for correcting the calculated depth on the basis of depths of other regions positioned respectively around the plurality of regions; fine adjustment means for selecting a depth closest to the corrected depth and at which the striped pattern sharpness is at an extreme; and means for extracting an image with a luminance on the basis of the selected depth.