Deep learning-based image outpainting of finger-vein image
Bibliographic record
Abstract
Despite fast authentication and user convenience, the lack of a fixed frame in contactless finger-vein acquisition causes missing regions and discrepancies between enrolled and query images, thereby degrading recognition performance. Existing image outpainting-based methods restore missing regions but often contain a large number of parameters, making them slow and unsuitable for real-time applications. To overcome these issues, this paper proposes a lightweight image outpainting network called knowledge distilled adaptive frequency attention network (KD-AFA-Net). KD-AFA-Net is based on a lightweight model that uses thinner separable U-Net with knowledge distillation (KD) from a high-performance teacher. In addition, to compensate for the limitations of convolutional neural networks (CNNs) in capturing global information, a novel adaptive frequency attention (AFA) module is designed. The AFA module decomposes intermediate features via a two-dimensional fast Fourier transform (FFT), learns the importance of high-frequency and low-frequency components, and emphasizes the important ones. Furthermore, this paper also proposes the AFA KD loss which enables the student model to effectively learn the frequency-domain refined outputs of the teacher’s AFA module. Moreover, we analyze recognition performance and use large language models (LLMs), ChatGPT-4o and ChatGPT-5 to prioritize experiments and to examine utilization strategies for future image-based tasks. Experiments on the Hong Kong Polytechnic University finger-image database version 1, the Shandong University machine learning and applications-homologous multi-modal traits (SDUMLA-HMT) finger-vein database, and the MMCBNU_6000 database show that KD-AFA-Net achieves equal error rates (EERs) of 2.56%, 3.49%, and 1.78% respectively, outperforming state-of-the-art image outpainting and KD methods while supporting real-time efficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".