Abstract
We present novel approaches involving generative adversarial networks and diffusion models in order to synthesize high quality, live and spoof fingerprint images while preserving features such as uniqueness and diversity. We generate live fingerprints from noise with a variety of methods, and we use image translation techniques to translate live fingerprint images to spoof. To generate different types of spoof images based on limited training data we incorporate style transfer techniques through a cycle autoencoder equipped with a Wasserstein metric along with Gradient Penalty (CycleWGAN-GP) in order to avoid mode collapse and instability. We find that when the spoof training data includes distinct spoof characteristics, it leads to improved live-to-spoof translation. We assess the diversity and realism of the generated live fingerprint images mainly through the Fréchet Inception Distance (FID) and the False Acceptance Rate (FAR). Our best diffusion model achieved a FID of 15.78. The comparable WGAN-GP model achieved slightly higher FID while performing better in the uniqueness assessment due to a slightly lower FAR when matched against the training data, indicating better creativity. Moreover, we give example images showing that a DDPM model clearly can generate realistic fingerprint images.
Abstract (translated)
我们提出了涉及生成对抗网络（GAN）和扩散模型的新颖方法，以在保留独特性和多样性特征的同时合成高质量、活体和假体指纹图像。我们使用多种方法从噪声中生成活指纹，并使用图像转换技术将活指纹图像转换为假体。为了根据有限训练数据生成不同类型的假体图像，我们在循环自动编码器（CycleAE）上配备了Wasserstein度量（Wasserstein）和梯度惩罚（CycleWGAN-GP），以避免模式坍塌和稳定性问题。我们发现，当假体训练数据包括显著的假体特征时，会导致活体到假体的翻译更好。我们通过费舍尔切比雪夫距离（FID）和假体接受率（FAR）来评估生成的活指纹图像的多样性和逼真度。我们的最佳扩散模型获得了15.78的FID。与训练数据上 slightly lower FAR 的 WGAN-GP 模型相比，具有更高的独特性评估成绩，表明在创意上表现更好。此外，我们还提供了生成真实指纹图像的示例图像，说明DDPM模型可以生成清晰逼真的指纹图像。
URL
https://arxiv.org/abs/2403.13916
