GenPalm: Contactless Palmprint Generation with Diffusion Models
This addresses a data bottleneck for researchers in biometrics and security, though it is incremental as it applies an existing generative method to a specific domain.
The paper tackles the scarcity of large-scale palmprint databases by introducing a novel palmprint generation method using diffusion probabilistic models, which enhances contactless palmprint recognition performance across several test databases.
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.