The scarcity of large-scale palmprint databases poses a significant
bottleneck to advancements in contactless palmprint recognition. To address
this, researchers have turned to synthetic data generation. While Generative
Adversarial Networks (GANs) have been widely used, they suffer from instability
and mode collapse. Recently, diffusion probabilistic models have emerged as a
promising alternative, offering stable training and better distribution
coverage. This paper introduces a novel palmprint generation method using
diffusion probabilistic models, develops an end-to-end framework for
synthesizing multiple palm identities, and validates the realism and utility of
the generated palmprints. Experimental results demonstrate the effectiveness of
our approach in generating palmprint images which enhance contactless palmprint
recognition performance across several test databases utilizing challenging
cross-database and time-separated evaluation protocols.

利用扩散概率模型介绍了一种新的手掌纹理生成方法，并开发了一个端到端框架来合成多个手掌身份，通过多个测试数据库的交叉数据库和时间分离评估协议证实了生成的手掌纹理图像在增强非接触式手掌纹理识别性能方面的有效性。