Low-FaceNet: Face Recognition-Driven Low-Light Image Enhancement
Bibliographic record
Abstract
Images captured in low-light conditions often induce the performance degradation of cutting-edge face recognition models. The missing and wrong face recognition inevitably makes vision-based systems operate poorly. In this paper, we propose Low-FaceNet, a novel face recognition-driven network to make low-level image enhancement (LLE) interact with high-level recognition for realizing mutual gain under a unified deep learning framework. Unlike existing methods, Low-FaceNet uniquely brightens real-world images by unsupervised contrastive learning and absorbs the wisdom of facial understanding. Low-FaceNet possesses an image enhancement network that is assembled by four key modules: a contrastive learning module, a feature extraction module, a semantic segmentation module, and a face recognition module. These modules enable Low-FaceNet to not only improve the brightness contrast and retain features but also increase the accuracy of recognizing faces in low-light conditions. Furthermore, we establish a new dataset of low-light face images called LaPa-Face. It includes detailed annotations with 11 categories of facial features and identity labels. Extensive experiments demonstrate our superiority against state-of-the-art methods of both LLE and face recognition even without ground-truth image labels. Our code and dataset are available at https://github.com/fanyihua0309/Low-FaceNet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".