Speckle Noise-Based Slice Generation for OCT Fingerprint Analysis

Abstract

Optical coherence tomography (OCT) is renowned for its high resolution and ability to capture the 3D structure of fingertip skin, significantly enhancing the anticounterfeiting capabilities of fingerprint recognition systems. However, the scarcity of OCT fingerprint datasets, exacerbated by data collection challenges and privacy concerns, poses a major hurdle for practical implementation. We propose a novel conditional diffusion model that generates highly realistic OCT fingerprints from segmentation masks, marking the first attempt to synthesize such images. By modifying the noise model in the diffusion process to account for speckle noise, our method achieves accurate noise simulation and effective removal, resulting in clearer detail feature generation. Subjective evaluations and multiple objective metrics confirm the superior visual quality and diversity of the generated images. By incorporating these images into training datasets for presentation attack detection (PAD) and fingerprint layer segmentation tasks, our method achieves pixel distributions highly consistent with bona fide fingerprints and learns detailed skin structures through segmentation mask guidance. These results highlight the potential of our approach to enhance the performance of OCT fingerprints in practical applications.