Author ORCID Identifier
Semester
Spring
Date of Graduation
2026
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Lane Department of Computer Science and Electrical Engineering
Committee Chair
Jeremy Dawson
Committee Member
Prashnna Gyawali
Committee Member
Nima Karimian
Abstract
The collection of biometric data is a labor-intensive, high-resource process that presents significant logistical, privacy, and cost barriers for researchers and developers. To address these challenges, the biometrics community has increasingly turned to generative models capable of producing synthetic datasets that reflect the statistical properties of real data. While substantial progress has been made in synthetic fingerprint generation for contact-based modalities, the contactless fingerphoto domain has remained largely underserved. This work presents a deep learning-based approach to synthetic contactless fingerphoto generation using a Stable Diffusion model guided by multimodal conditions (text and image). The dataset used for training was collected by the WVU Biometrics Lab using smartphones to captures fingerphotos in optimal and suboptimal conditions. The proposed framework generates synthetic contactless fingerphotos across five fingerprint pattern classes (Arch, Tented Arch, Left Loop, Right Loop, and Whorl) with structural fidelity enforced through Gabor ridge orientation maps that are guided using ControlNet to direct the diffusion process toward accurate ridge geometry and spatial frequency. A composite loss function incorporating perceptual, Gabor ridge, contrast, and FFT spectral components further constrains generation to preserve the meaningful structure of the contactless fingerprint domain. The resulting synthetic dataset is evaluated on fingerprint-specific image quality metrics, perceptual distributional fidelity, and matcher-based biometric utility, demonstrating the feasibility of large-scale synthetic contactless fingerphoto generation for use in training and evaluating contactless fingerprint recognition systems.
Recommended Citation
Burton, Christopher Harry, "Deep-Learning-Based Generation of Synthetic Contactless Fingerphotos" (2026). Graduate Theses, Dissertations, and Problem Reports. 13242.
https://researchrepository.wvu.edu/etd/13242