Locally adaptive texture features for multispectral face recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work introduces a new locally adaptive texture features for efficient multispectral face recognition. This new descriptor called Local Adaptive Ternary Pattern (LATP) is based on the Local Ternary Pattern (LTP). Unlike the previous techniques, this new descriptor determines the local pattern threshold automatically using local statistics. It shares with LTP the property of being less sensitive to noise, illumination change and facial expressions. These characteristics make it a good candidate for multispectral face recognition. Linear and non linear subspace learning and recognition techniques are introduced and used for performance evaluation of face recognition in the new texture space: PCA, LDA, Kernel-PCA (KPCA), Kernel-LDA (KDA), Linear Graph Embedding (LGE), Kernel-LGE (KLGE), Locality Preserving Projection (LPP) and Kernel-LPP (KLPP). The obtained results show an increase in recognition performance when texture features are used. LTP and LATP are the best performing techniques. The overall best performance is obtained in the short wave infrared spectrum (SWIR) using the new proposed technique combined with a non linear subspace learning technique.
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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 | 0.000 | 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 | 0.000 |
| 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