# Advancements in Synthetic Generation of Contactless Palmprint Biometrics Using StyleGAN Models  
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY 13676, USA; [EMAIL_ADDRESS] (A.M.M.C. ); [EMAIL_ADDRESS] (M.J.A.K.) Correspondence: mmtiaz@clarkson. limited availability of data, synthetic palmprints have been generated, and the generation tool has been made available for public use. This approach was motivated by previous works like Palm-GAN [13], which aimed to generate palmprint images using generative adversarial networks (GANs). The primary motivations for creating synthetic images are their affordability, effectiveness, and ability to provide increased privacy during testing. Moreover, significant advancements in the quality and resolution of images generated by GANs have been made recently [7, 8, 9]. A standard GAN generator's architecture operates similarly: initially, rough low-resolution attributes are created, which are progressively refined through upsampling layers. These features are blended locally using convolution layers, and additional details are added via nonlinear processes [13]. However, despite these apparent similarities, existing GAN structures do not generate images in a naturally hierarchical way. While broader features primarily influence the existence of enhanced details, they do not precisely determine their locations [12]. Instead, a significant portion of the more detailed information is determined based on fixed pixel coordinates. Synthetic data can be relied upon instead of real-world data [13]. A generator model can learn over training images to generate a synthetic image. In this scenario, synthetic data have the edge over real data regarding enrollment detection and verification [14]. A large number of synthetic datasets can be produced at low cost and with little effort while posing no privacy risk [15]. A single synthetic image with well-controlled modifications can also alter and expand the dataset [13]. The traditional method to develop synthetic images involves changing the orientation of images and using filters such as the Gabor filter, which changes the final structure of any image [5, 10, 16]. In other classical approaches, the orientation of the fingerprint or the skin color for facial biometrics is changed [11, 12]. There is no traditional approach to generating synthetic palmprints [14]. A framework for generating palm images using a style-based generator named StyleGAN2-ADA, a variation within the StyleGAN family, was previously introduced [1]. The current goal is to create synthetic images using different GANs like StyleGAN2-ADA and StyleGAN3 to demonstrate a more realistic transformation process. In this process, the position of each detail in the image will be entirely determined from the key features. This is the only StyleGAN-based approach to generate high-resolution palm images up to 2048 \(\times\) 2048 pixels. In a previous study, a TV-GAN-based framework was applied to generate palmprints; however, high-resolution images were not generated in that work [13]. The contributions are as follows:  
* Our model utilizes a high-resolution and progressive growth training approach, producing realistic shapes and hand-image boundaries without facing quality issues. * New quality metrics are developed to assess the usability of generated images and their similarity to original palm images, ensuring that the synthetic palm images do not reveal real identities. Figure 1: Palmprint feature definitions with principal lines and wrinkles [1]. * The generated model is publicly available, representing the first StyleGAN-based palm image synthesis model. * The SIFT (Scale-Invariant Feature Transform)-based method to filter unwanted images from the generated synthetic images is open-sourced. * A novel script to detect finger anomalies in the field of palmprint recognition is open-sourced. The rest of the paper is organized as follows. In Section 2, the pre-processing method and proposed model architecture are presented.