# Advancements in Synthetic Generation of Contactless Palmprint Biometrics Using StyleGAN Models
## 6 Results and Discussion  
### Model Training  
Each segment of the dataset took different amounts of time to train. For example, Training Session 1 (Table 1) took around 24 h. The rest of the training steps, depending on the step, took between 24 and 96 h.  
For StyleGAN2-ADA, the training time was approximately three weeks. The first training iteration appeared abnormal as the images transitioned from the original to new ones. During progress, the maximum number of iterations made a noticeable difference from the earlier iterations. For training sessions 1 and 2, 200 epochs were applied as they consisted of either the right or the left hand, while for sessions 3, 4, and 5 (mixed with left and right hand), training was stopped after 500 epochs as further epochs showed no significant change. Figure 8 presents 30 generated palmprint images over different epochs (0 th, 100 th, and 500 th) to illustrate the diversity among the generated images in terms of the position of the principal lines, the color of the palmprint, and their contrast.  
The truncation parameter \"trunc\" determines the variation in the generated images. The default value of 'trunc' is 0.5, which produces the original image. As the truncation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Observer 1**} & \multicolumn{2}{c}{**Observer 2**} \\ \hline
**Parameters** & **StyleGAN2-ADA** & **StyleGAN3** & **StyleGAN2-ADA** & **StyleGAN3** \\ \hline Shadow over Palm & 39 & 4 & 41 & 4 \\ \hline Image Imbalance & 23 & 4 & 49 & 10 \\ \hline Overlap with Two Palms & 25 & 0 & 27 & 2 \\ \hline Finger Issue & 15 & 5 & 26 & 6 \\ \hline No Palm Marker & 11 & 8 & 20 & 8 \\ \hline
**Total** & **113** & **21** & **163** & **30** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of anomalies detected through visual observation using 1560 images.  
Figure 8: Training situation of the palm photos (from00 epochs to 500 epochs).  
value increases, the images become clearer and more realistic, with the optimal threshold being 1.0. Beyond 1.0, increasing the truncation value further will result in greater diversity, but the images will no longer appear as realistic.  
For StyleGAN3, as shown in Figure 9, considerable enhancements in both the quality of the images and the stability of the model were observed during the training process. The final phase of the training resulted in the generation of high-quality images, showcasing the effectiveness of the StyleGAN3 architecture and the implemented training strategy. The model, saved as a (.pkl) file, demonstrated notable improvements in image quality, with enhanced resolution and more realistic texturing, which is a testament to the capabilities of the StyleGAN3 model and the tailored training approach.  
The varying training times highlight the computational intensity required for generating high-quality synthetic palm images, with StyleGAN2-ADA's lengthy process indicating its complexity. Early training iterations revealed instability, emphasizing the importance of adequate epochs to achieve stable and realistic outputs. Using the truncation parameter 'trunc' effectively balanced image realism and diversity, showing its critical role in refining model performance. StyleGAN3's superior results, with its ability to produce high-resolution and realistic images, demonstrate the advanced capabilities of its architecture and tailored training approach. These findings underline the significance of optimizing training strategies and parameters to enhance synthetic image quality and variability. This research confirms the potential of StyleGAN3 in advancing biometric authentication technologies by providing high-quality, diverse datasets essential for robust palmprint recognition systems.