Bibliographic record
Abstract
In this chapter, newly developed curvelet transform has been presented as a new tool for feature extraction from facial images. Various algorithms are discussed along with relevant experimental results as reported in some recent works on face recognition. Looking at the results presented in tables 1, 2 and 3, we can infer that curvelet is not only a successful feature descriptor, but is superior to many existing wavelet-based techniques. Results for only one standard database (ORL) are listed here; nevertheless, work has been done on other standard databases like, FERET, YALE, Essex Grimace, Georgia-Tech and Japanese facial expression datasets. From the results presented in all these datasets prove the superiority of curvelets over wavelets for the application of face recognition. Curvelet features thus extracted from faces are also found to be robust against noise, significant amount of illumination variation, facial details variation and extreme expression changes. The works on face recognition using curvelet transform that exist in literature are not yet complete and do not fully understand the capability of curvelet transform for face recognition; hence, there is much scope of improvement in terms of both recognition accuracy and curvelet-based methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".