High-speed biometrics ultrasonic system for 3D fingerprint imaging
Bibliographic record
Abstract
The objective of this research is to develop a new robust fingerprint identification technology based upon forming surface-subsurface (under skin) ultrasonic 3D images of the finger pads. The presented work aims to create specialized ultrasonic scanning methods for biometric purposes. Preliminary research has demonstrated the applicability of acoustic microscopy for fingerprint reading. The additional information from internal skin layers and dermis structures contained in the scan can essentially improve confidence in the identification. Advantages of this system include high resolution and quick scanning time. Operating in pulse-echo mode provides spatial resolution up to 0.05 mm. Technology advantages of the proposed technology are the following: • Full-range scanning of the fingerprint area "nail to nail” (2.5 x 2.5 cm) can be done in less than 5 sec with a resolution of up to 1000 dpi. • Collection of information about the in-depth structure of the fingerprint realized by the set of spherically focused 50 MHz acoustic lens provide the resolution ~ 0.05 mm or better • In addition to fingerprints, this technology can identify sweat porous at the surface and under the skin • No sensitivity to the contamination of the finger's surface • Detection of blood velocity using Doppler effect can be implemented to distinguish living specimens • Utilization as polygraph device • Simple connectivity to fingerprint databases obtained with other techniques • The digitally interpolated images can then be enhanced allowing for greater resolution • Method can be applied to fingernails and underlying tissues, providing more information • A laboratory prototype of the biometrics system based on these described principles was designed, built and tested. It is the first step toward a practical implementation of this technique.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".