Multibiometric System Using Level Set, Modified LBP and Random Forest
Why this work is in the frame
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Bibliographic record
Abstract
Multibiometric systems alleviate some of the shortcomings possessed by the unimodal biometrics and provide better recognition performance. This paper presents a multibiometric system that integrates the iris and face features based on the fusion at the feature level. The proposed multibiometric system has three novelties as compared to the previous works. First, distance regularized level-set evolution (DRLSE) technique is utilized to localize the iris and pupil boundary from an iris image. The DRLSE maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Second, a modified local binary pattern (MLBP), which combines both the sign and magnitude features for the improvement of recognition performance, is applied. Third, to select the optimal subset of features from the fused feature vector, a feature subset selection scheme based on random forest (RF) is proposed. To evaluate the performance of the proposed scheme, the facial images of Yale Extended B Face database are fused with the iris images of CASIA V4 interval dataset to construct an iris–face multimodal biometric dataset. The experimental results indicate that the proposed multimodal biometrics system is more reliable and robust than the unimodal biometric scheme.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it