Palm vein recognition has emerged as a promising biometric technology, yet its development remains constrained by the scarcity of large-scale publicly available datasets. Several methods of palm vein image generation have been proposed to address this issue. These methods usually focus on the anatomical realism of palm vein patterns, but overlook the biophysical correlation between identities and vein patterns, particularly in simulating identity-specific vein contrast. To tackle this limitation, we propose a novel biophysics-driven synthesis method. Our method constructs a 3D palm vascular tree via established modeling method. Then, a projection model is proposed to map the 3D tree into 2D space to derive palm vein patterns. The projection model is based on skin spectral absorption and simulates the natural attenuation of light passing through the skin using a layer integration method. For different identities, we sample different skin parameters, resulting in varying degrees of attenuation. This method effectively simulates the variation in vein contrast across different identities. Furthermore, we introduce a conditional diffusion model that uses the projected patterns as identity conditions to generate palm vein images. To the best of our knowledge, this is the first palm vein generation method based on the diffusion model. Experimental results demonstrate that our method not only outperforms existing methods, but also enables a recognition model trained on our synthetic data to achieve superior performance compared to a model trained on real-world data at a scale of 2,000 IDs under an open-set protocol with a TAR@FAR=1:1 of 1e-4.

We address event-based deblurring for low-light imaging, where conventional frames suffer severe blur, noise and saturation, while events capture sharp high-frequency contrast changes with microsecond latency that can guide the recovery of lost structures. Existing event-based reconstruction methods neither explicitly model low-light noise and saturation nor enforce precise alignment between events and frames, which limits cross-modal fusion and deblurring quality. We propose the Wiener-Deconvolution-Driven Event-Based Deblurring Network (WiED-Net), which embeds the Wiener deconvolution into a deep architecture so that the physical imaging model and noise statistics are encoded in the frequency domain and high-frequency recovery is stabilized on noise dominated night data. WiED-Net adopts a two stage design. The first stage applies Wiener deconvolution in both image and feature spaces to suppress noise, recover saturated regions and reduce ringing, assisted by an eventguided cross-modal feature fusion (ECFF) module for accurate alignment. The second stage uses a multi-scale fusion module to integrate the complementary event and image branches. Training is constrained by a set of losses, including a tailored blur kernel loss that provides closed-loop regularization from physical priors. Together, these designs enable WiED-Net to recover fine details while robustly suppressing artifacts and noise, and to achieve superior quantitative and qualitative performance, achieving superior quantitative and qualitative performance with a notable improvement of 1.97 dB in PSNR and 5% in SSIM over the previous state-of-the-art methods in low-light deblurring. Code will be available at https://github.com/zhuzifeng38/WiED-Net.

Palm vein recognition is an emerging biometric technology that offers enhanced security and privacy. However, acquiring sufficient palm vein data for training deep learning-based recognition models is challenging due to the high costs of data collection and privacy protection constraints. This has led to a growing interest in generating pseudo-palm vein data using generative models. Existing methods, however, often produce unrealistic palm vein patterns or struggle with controlling identity and style attributes. To address these issues, we propose a novel palm vein generation framework named PVTree. First, the palm vein identity is defined by a complex and authentic 3D palm vascular tree, created using an improved Constrained Constructive Optimization (CCO) algorithm. Second, palm vein patterns of the same identity are generated by projecting the same 3D vascular tree into 2D images from different views and converting them into realistic images using a generative model. As a result, PVTree satisfies the need for both identity consistency and intra-class diversity. Extensive experiments conducted on several publicly available datasets demonstrate that our proposed palm vein generation method surpasses existing methods and achieves a higher TAR@FAR=1e-4 under the 1:1 Open-set protocol. To the best of our knowledge, this is the first time that the performance of a recognition model trained on synthetic palm vein data exceeds that of the recognition model trained on real data, which indicates that palm vein image generation research has a promising future.

Palmprint recognition is significantly limited by the lack of large-scale publicly available datasets. Previous methods have adopted Bézier curves to simulate the palm creases, which then serve as input for conditional GANs to generate realistic palmprints. However, without employing real data fine-tuning, the performance of the recognition model trained on these synthetic datasets would drastically decline, indicating a large gap between generated and real palmprints. This is primarily due to the utilization of an inaccurate palm crease representation and challenges in balancing intra-class variation with identity consistency. To address this, we introduce a polynomial-based palm crease representation that provides a new palm crease generation mechanism more closely aligned with the real distribution. We also propose the palm creases conditioned diffusion model with a novel intra-class variation control method. By applying our proposed K-step noise-sharing sampling, we are able to synthesize palmprint datasets with large intra-class variation and high identity consistency. Experimental results show that, for the first time, recognition models trained solely on our synthetic datasets, without any fine-tuning, outperform those trained on real datasets. Furthermore, our approach achieves superior recognition performance as the number of generated identities increases.

The lack of large-scale data seriously hinders the development of palmprint recognition. Recent approaches address this issue by generating large-scale realistic pseudo palmprints from Bézier curves. However, the significant difference between Bézier curves and real palmprints limits their effectiveness. In this paper, we divide the Bézier-Real difference into creases and texture differences, thus reducing the generation difficulty. We introduce a new palm crease energy (PCE) domain as a bridge from Bézier curves to real palmprints and propose a two-stage generation model. The first stage generates PCE images (realistic creases) from Bézier curves, and the second stage outputs realistic palmprints (realistic texture) with PCE images as input. In addition, we also design a lightweight plug-and-play line feature enhancement block to facilitate domain transfer and improve recognition performance. Extensive experimental results demonstrate that the proposed method surpasses state-of-the-art methods. Under extremely few data settings like 40 IDs (only 2.5% of the total training set), our model achieves a 29% improvement over RPG-Palm and outperforms ArcFace with 100% training set by more than 6% in terms of TAR@FAR=1e-6.

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the Bézier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art BézierPalm by more than 5% and 14% in terms of TAR@FAR=1e-6 under the 1 : 1 and 1 : 3 Open-set protocol. When accessing only 10% of the real training data, our method still outperforms ArcFace with 100% real training data, indicating that we are closer to real-data-free palmprint recognition.