  This study explores the generation of synthesized fingerprint images using
Denoising Diffusion Probabilistic Models (DDPMs). The significant obstacles in
collecting real biometric data, such as privacy concerns and the demand for
diverse datasets, underscore the imperative for synthetic biometric
alternatives that are both realistic and varied. Despite the strides made with
Generative Adversarial Networks (GANs) in producing realistic fingerprint
images, their limitations prompt us to propose DDPMs as a promising
alternative. DDPMs are capable of generating images with increasing clarity and
realism while maintaining diversity. Our results reveal that DiffFinger not
only competes with authentic training set data in quality but also provides a
richer set of biometric data, reflecting true-to-life variability. These
findings mark a promising stride in biometric synthesis, showcasing the
potential of DDPMs to advance the landscape of fingerprint identification and
authentication systems.
