Convolutional neural network for distortion Classification in face images.
Bibliographic record
Abstract
Face processing algorithms are becoming more popular in recent days due to the great domain of application in which they can be used.As a consequence, research about the quality of face images is also increasing.The current approach to Face Image Quality Assessment (FIQA) is focused on improving the performance of face recognition systems, as a result, current FIQA algorithms don't provide an indication of quality, but a performance estimation for face recognition algorithms.This approach makes the FIQA algorithms potentially unsuited for other scenarios regarding face images, and susceptible to inherit the limitations of face recognition.The present work tackles the main limitations of the current FIQA algorithms by proposing a new approach based on the distortions affecting the images.We developed two models based on Convolutional Neural Networks (CNN), to classify facial images according to the type and the degree of the distortion present in them.The models' output provides qualitative information about the quality of facial images, useful for face recognition systems, as well as other face processing algorithms.Additionally, the proposed method can be a starting point to image enhancement processes like denoising, and deblurring.Two other contributions can be outlined from this work: a comprehensive study about the impact of blur, noise, brightness, contrast, and JPEG compression in face processing algorithms; and a new dataset for image quality assessment and distortion classification in face images.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".