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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".