Biometrics Face Recognition Using Method of Wavelet and Curvelet Transforms with COVID-19
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
During last two decades the subject of face recognition has become a major issue. It has been used in several important real-world applications such as database security, video surveillance, smart card, internet and intranet. The people’s ability to recognize a face is reduced if the person has a mask covering the lower part of face, so it may be focus now on, the eyes and the individual features on upper part of face. To extract the features, the Wavelet and Curvelet transforms proved its efficiency due to its higher for detection of curves and lines, which recognize the human’s face, these features are used to identify the enrolled persons and it should be stored in the template system to be used later in the recognition system. The result of this paper is to recognize the face of person with mask. The system performance was evaluated depend on face database (kaggle) for face-recognition with face mask during COVID-19 period. The results indicate that the proposed system showed good results, and it outperforms the other algorithms that used in face-recognition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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