Optical coherence tomography used for internal biometrics
Bibliographic record
Abstract
Traditional biometric technologies used for security and person identification essentially deal with fingerprints, hand geometry and face images. However, because all these technologies use external features of human body, they can be easily fooled and tampered with by distorting, modifying or counterfeiting these features. Nowadays, internal biometrics which detects the internal ID features of an object is becoming increasingly important. Being capable of exploring under-skin structure, optical coherence tomography (OCT) system can be used as a powerful tool for internal biometrics. We have applied fiber-optic and full-field OCT systems to detect the multiple-layer 2D images and 3D profile of the fingerprints, which eventually result in a higher discrimination than the traditional 2D recognition methods. More importantly, the OCT based fingerprint recognition has the ability to easily distinguish artificial fingerprint dummies by analyzing the extracted layered surfaces. Experiments show that our OCT systems successfully detected the dummy, which was made of plasticene and was used to bypass the commercially available fingerprint scanning system with a false accept rate (FAR) of 100%.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".