PalmDiff: When Palmprint Generation Meets Controllable Diffusion Model

Abstract

Due to its distinctive texture and intricate details, palmprint has emerged as a critical modality in biometric identity recognition. The absence of large-scale public palmprint datasets has substantially impeded the advancement of palmprint research, resulting in inadequate accuracy in commercial palmprint recognition systems. However, existing generative methods exhibit insufficient generalization, as the images they generate differ in specific ways from the conditional images. This paper proposes a method for generating palmprint images using a controllable diffusion model (PalmDiff), which addresses the issue of insufficient datasets by generating palmprint data, improving the accuracy of palmprint recognition. We introduce a diffusion process that effectively tackles the problems of excessive noise and loss of texture details commonly encountered in diffusion models. A linear attention mechanism is employed to enhance the backbone’s expressive capacity and reduce the computational complexity. To this end, we proposed an ID loss function to enable the diffusion model to generate palmprint images under the same identical space consistently. PalmDiff is compared with other generation methods in terms of both image quality and the enhancement of palmprint recognition performance. Experiments show that PalmDiff performs well in image generation, with an FID score of 13.311 on MPD and 18.434 on Tongji. Besides, PalmDiff has significantly improved various backbones for palmprint recognition compared to other generation methods.