Manifold Learning of Overcomplete Feature Spaces in a Multimodal Biometric Recognition System of Iris and Palmprint
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a bimodal biometric recognition system based on iris and palmprint. Different wavelet-based filters including log Gabor, Discrete Cosine Transform (DCT), Walsh and Haar are used to extract features from images. Then we fuse iris and palmprint at the feature level by concatenating the feature vectors from two modalities. Since wavelet transforms generate huge number of features, a dimensionality reduction step is necessary to make the classification and matching steps tractable and computationally feasible. In this paper, two well-known dimensionality reduction algorithms including Laplacian eigenmaps and Singular Value Decomposition (SVD) are used to reduce the size of feature space. Applying these dimensionality reduction methods not only decreases the computational cost of matching remarkably but also it improves the accuracy of recognition by reducing the unnecessary model complexity. Eventually multiple classification techniques are used in the transformed feature spaces for the final matching and recognition. CASIA datasets for iris and palmprint are used in this study. The experiments show the effectiveness of our feature level fusion method and also the dimensionality reduction methods we used. Based on our experiments, our multimodal biometric system always outperforms the unimodal recognition systems with higher accuracy. Moreover, an appropriate dimensionality reduction algorithm always helps to improve the accuracy of classifier. Finally, the log Gabor filter extracts the most discriminative features from images compared to other wavelet transforms.
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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