Facial Anthropometry-Based Masked Face Recognition System
Bibliographic record
Abstract
Different kinds of occlusion have proven to disrupt the accuracy of face recognition systems, one of them being masks. The problem of masked faces has become even more apparent with the widespread of the COVID-19 virus, with most people wearing masks in public. This brings up the issue of existing face recognition systems been able to accurately recognize people even when part of their face and the major identifiers (such as the nose and mouth) are covered by a facemask. In addition, most of the databases that have been curated in different organizations, countries are majorly of non-masked faces, and masked databases are rarely stored or universally accepted compared with conventional face datasets. Therefore, this paper aim at the development of a Masked Face Recognition System using facial anthropometrics technique (FAT). FAT is the science of calculating the measurements, proportion and dimension of human face and their features. A dataset of faces with individual wearing medical face mask was curated. Using the Facial anthropometry based technique a Masked Face Recognition System developed. This system was implemented using Local Binary Patterns Histogram algorithms for recognition. On testing the developed system trained with unmasked dataset, show a high recognition performance of 94% and 96.8% for masked and non-masked face recognition respectively because of the Facial anthropometry based technique adapted. On deployment, users were been recognized when they are wearing a mask with part of their face covered in real-time.
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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) | 0.000 | 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) | 0.005 | 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".