Deep learning-based image outpainting of finger-vein image

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.