# Synthetic Image Generation Using Conditional GAN-Provided Single-Sample Face Image
## 5 Results and Discussion
### Comparison between Our Method and State of the Art Method  
In this study, we conducted a comprehensive benchmark comparison with the current state-of-the-art (SOTA) facial recognition methods. We specifically compared our approach to the techniques presented in [19; 20], which are known for their high accuracy under controlled shooting conditions. However, these methods often underperform in less stable environments, a significant aspect that is critical for practical applications. For instance, the method described in [19] is highly dependent on specific lighting and facial positioning, reducing its robustness to variable conditions typically encountered in real-world settings.  
Furthermore, the method described in [17; 26] shows a significant decline in performance when confronted with complex facial variations and variable conditions such as anger, disguise, and changes in illumination. These challenges necessitate the need for models that are capable of maintaining high accuracy across diverse conditions. Our approach attempts to bridge this gap, as evidenced by the comparative analysis presented in Figure 12.  
In addition to this analysis, further comparative results are detailed in Table 2. This table evaluates the performance of several state-of-the-art models, including VGG-Face [37], ResNet-50 [38], FaceNet [39], and DeepFace [40], with a specific focus on addressing the challenges posed by the SSPP problem. We also maintained consistent dataset configurations across all tests, as described in Section 4 of the manuscript. Overall, our proposed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Accuracy w/o** & \multicolumn{2}{c}{**Accuracy w/**} \\  & **Generated Images** & **Generated Images** & **Change in Accuracy** \\ \hline SSAE [19] & 65.31\% & 85.56\% & +20.25\% \\ SAS [20] & 68.34\% & 89.87\% & +21.53\% \\ OSL [17] & 65.23\% & 87.22\% & +21.99\% \\ SSFD [26] & 62.12\% & 90.21\% & +28.09\% \\ VGG-Face [37] & 75.45\% & 99.21\% & +23.76\% \\ ResNet-50 [38] & 76.55\% & 99.55\% & +23.00\% \\ FaceNet [39] & 75.88\% & 99.01\% & +23.13\% \\ DeepFace [40] & 76.89\% & 99.21\% & +22.32\% \\ CustomCNN & 68.00\% & 91.12\% & +23.12\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: In this table, we compare the accuracy of each model with and without generated images.  
Figure 12: Accuracy comparison with state-of-the-art SSPP methods [17; 19; 20; 26].  
model demonstrates higher accuracy in both scenarios when trained with and without the generated images.  
Initial tests on our SSPP dataset revealed a significant decrease in recognition accuracy of even pretrained state-of-the-art models, as shown in Table 2. However, subsequent fine-tuning of these models with our CGAN-generated images led to notable improvements in accuracy. This not only underscores the effectiveness of our CGAN model in generating high-quality synthetic samples but also demonstrates its utility in enhancing the performance of existing algorithms under SSPP conditions.  
Our approach not only surpasses these previous techniques in recognition accuracies but also showcases a significant advantage in terms of dataset versatility. Unlike prior methods that often rely on large, diverse, and specific datasets for training, our model exhibits dataset independence. After initial training on the AR dataset, our system can generate synthetic images encompassing a wide range of facial expressions and variations from just a single neutral expression. This capability not only enhances the robustness of our model but also provides a solution for mitigating the common data scarcity in facial recognition research.