Masked Face Recognition Using Convolutional Neural Networks and Similarity Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Nowadays, human face recognition systems have been widely used in different applications in which identity recognition is needed. The performance of current face recognition algorithms is negatively affected by occlusions, such as facial masks and various human poses. To address these challenges, we re-trained a modified version of the VGG19 deep learning model on masked and unmasked images of 62 identities to design a feature extractor that extracts deep features from the non-occluded areas of the face. This feature extractor is combined with our proposed similarity analysis network that is trained on our dataset to automatically judge whether the masked and unmasked images correspond to the same or different identities. Our final approach consists of a feature extractor from a fine-tuned VGG19 and a similarity model. It achieved an accuracy of 80 to 85 percent in recognizing the identity of test masked images with different poses.
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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 | 0.003 |
| 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