Version 1
: Received: 20 December 2023 / Approved: 21 December 2023 / Online: 21 December 2023 (14:04:58 CET)
How to cite:
Chowdhury, A. M. M.; Khondkar, M. J. A.; Imtiaz, M. H. Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3. Preprints2023, 2023121655. https://doi.org/10.20944/preprints202312.1655.v1
Chowdhury, A. M. M.; Khondkar, M. J. A.; Imtiaz, M. H. Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3. Preprints 2023, 2023121655. https://doi.org/10.20944/preprints202312.1655.v1
Chowdhury, A. M. M.; Khondkar, M. J. A.; Imtiaz, M. H. Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3. Preprints2023, 2023121655. https://doi.org/10.20944/preprints202312.1655.v1
APA Style
Chowdhury, A. M. M., Khondkar, M. J. A., & Imtiaz, M. H. (2023). Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3. Preprints. https://doi.org/10.20944/preprints202312.1655.v1
Chicago/Turabian Style
Chowdhury, A. M. M., Md Jahangir Alam Khondkar and Masudul Haider Imtiaz. 2023 "Advancements in Synthetic Generation for Contactless Palmprint Biometrics Using StyleGan2-ADA and StyleGAN3" Preprints. https://doi.org/10.20944/preprints202312.1655.v1
Abstract
In addressing the challenges of data scarcity in biometrics, this study explores the generation of synthetic palmprint images as an efficient, cost effective, and privacy preserving alternative to real-world data reliance. Traditional methods for synthetic biometric image creation primarily involve orientation modifications and filter applications, with no established method specific to palmprints. We introduced the utilization of the “Style-based generator”, StyleGAN2-ADA, from the StyleGAN series, renowned for generating high-quality images. Furthermore, we explore the capabilities of its successor, StyleGAN3, which boasts enhanced image generation, facilitating smooth and realistic transitions. By comparing the performance of StyleGAN3 on public dataset, we aim to establish the most efficient generative model for this purpose. Evaluations were conducted using the SIFT (Scale-Invariant Feature Transform) algorithm into our evaluation framework. Preliminary findings suggest that StyleGAN3 offers superior generative capabilities, enhancing equivariance in synthetic palmprint generation.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
