Synthetic generation of finger-vein region by feature fusion-based enhanced U-transformer for finger-vein recognition
Bibliographic record
Abstract
Non-contact finger-vein recognition device offers high user convenience and minimizes hygienic issues, but it lacks a separate guide to support the finger region. Therefore, recognition performance can decline if recognized image includes areas not present in enrolled image. Previous approaches to address this issue still fail to overcome performance degradation when critical features are located in missing areas of the recognized image compared to the enrolled image. Therefore, this study proposes the method of synthetic generation of finger-vein region by feature fusion-based Enhanced U-transformer for finger-vein recognition. Enhanced U-transformer enhances recognition performance by outpainting missing finger-vein regions in recognized image using feature fusion-based U-shaped transformer. This improvement is achieved through modified cross-attention, residual layers, structural similarity index measure loss, and absolute positional embedding. The experiment utilized the Hong Kong Polytechnic University finger-image database version 1 and the Shandong University machine learning and applications-homologous multi-modal traits (SDUMLA-HMT) finger-vein database. Finger-vein recognition using Enhanced U-transformer achieved equal error rates of 3.01 % and 4.33 % in these databases, respectively, surpassing the performance of state-of-the-art methods. In addition, our Enhanced U-transformer demonstrates its ability to operate on embedded system with low computational resources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".