GCSTLPP: Face Recognition using Gabor Center-Symmetric Tensor Locality Preservative Projection Approach in Video
Bibliographic record
Abstract
Face Recognition has become the challenging and interesting research topic in the last few years.The aim is to design a robust Face Recognition System under different environmental conditions like illumination, pose and occlusion.These are the three major challenges in Face Recognition which may hinder the Face Recognition system.By combining the three successful representations such as Gabor filters, CS-LBP and TLPP better performance can be achieved as compared to just considering them individually.CS-LBP is used for describing interest regions which have good tolerance to illumination and computational efficiency and TLPP is used to take the data directly in the form of tensors as input.Since the number of the combined feature sets are more only a few feature sets is selected to be trained by the Support Vector Machine classifier.A number of experiments are conducted using YouTube celebrity, McGill Face dataset and as well as the own collected sequence under different conditions such as illumination variations, different poses, occlusion including indoor and outdoor scenes.This approach provides better results compared to traditional approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".