A Novel Kernelized Face Recognition System
Bibliographic record
Abstract
Face recognition is a quintessential biometric technique. It still remains challenging to accurately characterize the identity related features in face images. In this paper, we propose a novel classification method based on Kernel Fisher Discriminant Analysis using the distinctiveness of Gabor features and the robustness of ordinal measures. These parameters are derived from magnitude, phase, real and imaginary responses of Gabor filtering, respectively, and then are combined as visual primitive in local regions. The statistical distribution of these primitives in face image blocks are concatenated to obtain a feature vector whose dimension is reduced using PCA and variance. Finally, each feature vector is considered as a feature input for the proposed Multi-Class KFD classifier based on RBF Kernel. The proposed method is tested on the well-known ORL face database and the Yale face database. Then, it is evaluated and compared with linear classifier (LDA) in term of classification accuracy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".