Finger Vein Recognition Based on Multi-Features Fusion
Bibliographic record
Abstract
Biometric Recognition Systems allow individuals to be automatically authenticated or identified by using their unique characteristics.Finger vein (FV), widely used for this purpose, has a crucial place among biometric systems because of its advantages, which are user-friendliness, ability to detect living tissue, high reliability, low system cost, and less area requirement in installation.It has a wide usage area, especially in places where personal safety is at the forefront.In this study, we examine the effect of the Horizontal and Vertical Total Proportion (HVTP) feature extraction algorithm on the success rate when the fusion technique is applied.Homomorphic Filter (HF) and Perona-Malik Anisotropic Diffusion (PMAD) are used to remove the noise and light scattering issue in the FV databases, and Gray Level Run Length Matrices (GLRLM), Gray Level Co-occurrence Matrices (GLCM), Segmentation-based Fractal Texture Analysis (SFTA), Horizontal Total Proportion (HTP), and Vertical Total Proportion (VTP) methods are applied to describe texture features.The fusion of multiple features instead of using only one type of feature can improve the accuracy of FV recognition systems.The novelty of the study is the fusion of HTP and VTP with the GLRLM, GLCM, and SFTA features by using Yang finger vein databases (Database_1) and MMCBNU_6000 (Database_2).Experimental results reveal that the HTP and VTP significantly improved the classification success in these FV image databases.The best success rate achieved in the Ensemble classifier is 99.7% using Database_1 and 97.6% using Database_2.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".