扩散模型用于指纹生成

… focus on generating fingerprint patches and transforming fingerprints using diffusion models… of diffusion models to refine noise images, thereby generating diverse and realistic synthetic …

This paper reviews recent progress in fingerprint image generation using deep generative models, with a focus on Generative Adversarial Network (GAN)-based and diffusion-based approaches. Fingerprint data are essential for biometric recognition, but collecting large and diverse datasets is difficult, especially for latent fingerprints. Early methods based on physical or statistical modeling could not produce realistic textures or sufficient diversity. With the development of deep learning, GAN-based models such as FingerGAN, PrintsGAN, and lightweight GANs have significantly improved the realism of generated fingerprints by learning data distributions directly. These methods introduce techniques such as structural constraints, multi-stage generation, and improved loss functions to enhance image quality and stability. However, GAN-based models still suffer from problems such as training instability, mode collapse, and limited control over identity consistency. To address these issues, diffusion models have recently been introduced into fingerprint generation. By gradually denoising random noise, diffusion models can generate high-quality and diverse fingerprint images with better stability. Advanced diffusion frameworks further enable controllable generation, allowing users to adjust fingerprint attributes such as style, quality, and sensor type while preserving identity information. Overall, diffusion-based methods show strong potential to become the next generation of fingerprint synthesis techniques.

… [15] proposed a fingerprint generation algorithm based on GAN. With the continuous improvement of GAN models, Engelsma et al. [8] proposed PrintsGAN in 2015, which combines …

… forgery a uniquely challenging problem for both generation … to fingerprints synthesized by other GAN variants or diffusion-… generator, is a critical requirement for practical fingerprint …

The advancement of fingerprint research within public academic circles has been trailing behind facial recognition, primarily due to the scarcity of extensive publicly available datasets, despite fingerprints being widely used across various domains. Recent progress has seen the application of deep learning techniques to synthesize fingerprints, predominantly focusing on large-area fingerprints within existing datasets. However, with the emergence of AIoT and edge devices, the importance of tiny partial fingerprints has been underscored for their faster and more cost-effective properties. Yet, there remains a lack of publicly accessible datasets for such fingerprints. To address this issue, we introduce publicly available datasets tailored for tiny partial fingerprints. Using advanced generative deep learning, we pioneer diffusion methods for fingerprint synthesis. By combining random sampling with inpainting diffusion guided by feature keypoints masks, we enhance data augmentation while preserving key features, achieving up to 99.1% recognition matching rate. To demonstrate the usefulness of our fingerprint images generated using our approach, we conducted experiments involving model training for various tasks, including denoising, deblurring, and deep forgery detection. The results showed that models trained with our generated datasets outperformed those trained without our datasets or with other synthetic datasets. This indicates that our approach not only produces diverse fingerprints but also improves the model’s generalization capabilities. Furthermore, our approach ensures confidentiality without compromise by partially transforming randomly sampled synthetic fingerprints, which reduces the likelihood of real fingerprints being leaked. The total number of generated fingerprints published in this article amounts to 818,077. Moving forward, we are ongoing updates and releases to contribute to the advancement of the tiny partial fingerprint field. The code and our generated tiny partial fingerprint dataset can be accessed at https://github.com/Hsu0623/Inpainting-Diffusion-Synthetic-and-Data-Augment-with-Feature-Keypoints-for-Tiny-Partial-Fingerprints.git

High-quality, diversified, and large-scale datasets are crucial for creating reliable deep-learning models for biometric applications. Unfortunately, there is a shortage of well-labeled data. This paper introduces a text-conditional biometric imaging generation framework, addressing the complexities associated with multi-modality considerations. The proposed framework harnesses cutting-edge diffusion probabilistic models to produce multi-modal biometric images at high resolutions, seamlessly aligning with biometric language prompts. The experimental results unequivocally validate the efficacy of the proposed framework in generating a diverse array of highly realistic synthetic biometric images while consistently maintaining a commendable level of fidelity when juxtaposed with their respective reference datasets. The contributions of this study offer substantial potential for propelling advancements in biometric imaging research.

The majority of contemporary fingerprint synthesis is based on the Generative Adversarial Network (GAN). Recently, the Denoising Diffusion Probabilistic Model (DDPM) has been demonstrated to be more effective than GAN in numerous scenarios, particularly in terms of diversity and fidelity. This research develops a model based on the enhanced DDPM for fingerprint generation. Specifically, the image is decomposed into sub-images of varying frequency sub-bands through the use of a wavelet packet transform (WPT). This method enables DDPM to operate at a more local and detailed level, thereby accurately obtaining the characteristics of the data. Furthermore, a polynomial noise schedule has been designed to replace the linear noise strategy, which can result in a smoother noise addition process. Experiments based on multiple metrics on the datasets SOCOFing and NIST4 demonstrate that the proposed model is superior to existing models.

Fingerprints have been crucial evidence for law enforcement agencies for a long time. Though the rapidly developing deep learning has dramatically improved the performance of the latent fingerprint recognition algorithm, a fully automated latent fingerprint identification system is still far from meeting actual needs. One major issue is the lack of publicly available latent fingerprint databases. Recently, diffusion probabilistic models have emerged as state-of-the-art deep generative methods for image synthesis. These models have better distribution coverage and less mode collapse than the popular Generative Adversarial Networks. In this paper, we propose an end-to-end latent fingerprint synthetic approach based on the improved denoising diffusion probabilistic model. The proposed approach could simultaneously generate latent, rolled, and plain fingerprints of high visual realism. Several primary degradation factors, such as various background textures, limited area of ridge patterns, and structural noise, can be directly generated without any postprocessing, unlike existing methods. We conduct NFIQ2 and perceptual analysis in the experiments to evaluate the proposed approach. The results indicate that the quality and visual realism of the proposed synthetic fingerprints is similar to the natural ones, demonstrating the effectiveness of our approach.

… of generative diffusion models (GDM), we propose a conditional generative diffusion model (… bound (ELBO) of the log-conditional marginal distribution of the observed finegrained CF. …

Fingerprint-based indoor positioning systems require exhaustive site-surveying processes to establish the radio maps of wireless signals, such as Wi-Fi, at the target sites for localization, which are extremely time-consuming. Unfortunately, a rough, coarse-grained site-surveying run gathers inadequate radio fingerprints to produce lower-resolution radio maps. To address such a time-versus-accuracy dilemma, this paper proposes WIFIND, a novel approach employing diffusion models to generate additional details to reinforce the collected fingerprints. WIFIND employs a diffusion model trained on real-world fingerprint data to generate synthetic Channel State Information (CSI) that mimics actual Wi-Fi signals. Using a Spatially Conditioned Diffusion Model (SCDM), SCDM generates synthetic CSI data based on the given position, ensuring the data accurately reflects the spatial variations of Wi-Fi signals within the environment. This results in a richer and more diverse dataset, enhancing the model's ability to localize devices precisely. Extensive experiments demonstrate that WIFIND not only improves upon the baseline models trained with actual data but also outperforms existing state-of-the-art methods for CSI data augmentation. Preliminary results on the Ultra Dense Indoor MaMIMO dataset show that WIFIND achieves improvements ranging from 47.56% to 78.53% compared to the baseline, respectively.

… An example is DDPM [22], which uses diffusion probabilistic models based on denoising score matching. Recently, ILVR [8] proposed a method to guide and condition the generative …

… of synthetic data augmentation approaches enabled by generative models such as GANs, diffusion … In the domain of fingerprint biometrics, Grosz and Jain [67] presented GenPrint, a …