Bibliographic record
Abstract
Fingerprinting is the most extensively used biometrics supported by biggest database compared to other biometrics, such as retina imaging, face or voice recognition and others.However fingerprinting image could be distorted by pressure of the finger against the scanner, therefore needs to be contactless.Most important, distances between ridges on a finger depend on physical conditions (health) of an individual.That is when map of blood vessels in a finger is very helpful and supportive information.In addition, police criminal investigators do find some time not standalone fingerprint but images of few fingers or even image of a palm.In current study, we report a new design and test results of c o n t a c t l e s s line scan hardware, which produces images of single nail-to-nail finger, four fingers together, and image of human palm.The major focus of the study is development of high-resolution images of blood vessels and the new algorithm based on linear filtering neighborhood analysis, which generates a well-defined and interconnected blood vessel map.The new position of I R light sources provides a good and mostly uniform contrast between the veins and surrounding tissues.This configuration is different from the conventional positioning, where all three objects are aligned along vertical axis, that is, the source of light positioned above the tested finger, and the camera is located below the finger.The new experimental imaging configuration and blood vessel tracking algorithm could be combined with contactless fingerprinting to reinforce biometric personal identification.
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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) | 0.000 | 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) | 0.013 | 0.007 |
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".