## 4 Experiments
### Results on HELEN
#### 4.2.2 Ablation Study  
**Quantitative Analysis.** To further analyze the effectiveness of the key components in our proposed method, we conducted ablation experiments on the HELEN dataset, focusing on the contributions of the CCR-FE and SimFA modules. The baseline model is the standard Deeplabv3+ network. As shown in Table 2, on the one hand, the CCR-FE module enhances the representation of facial boundary details by focusing on critical facial features, resulting in improvements of 1.5% in Mean IoU and 1.2% in the overall F1-score. On the other hand, the SimFA module significantly improves the recovery of semantic information, leading to gains of 3.3% in Mean IoU and 2.0% in the overall F1-score. Furthermore, when both modules are integrated, the model achieves the best performance with a Mean IoU of 82.5% and an overall F1-score of 92.2%. These results clearly demonstrate the positive impact of feature enhancement and detail preservation on segmentation performance.  
**Qualitative Analysis.** To further illustrate the effect of different modules on facial segmentation performance, we selected representative samples from the HELEN test set for visual analysis. As shown in Figure 4, the baseline model without any enhancement modules exhibits noticeable boundary blurring and prediction discontinuities in critical regions such as the lip contours and eye outlines. After introducing the CCR-FE module, the model's ability to preserve structural integrity in boundary areas is significantly enhanced, enabling more accurate capture of the geometric shapes and boundaries of facial organs. When the SimFA module is incorporated, the overall semantic structure of the segmentation result becomes more developed, with substantially improved performance in texture  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **Skin** & **Nose** & **U-Lip** & **I-Mouth** & **L-Lip** & **Eyes** & **Brows** & **Mouth** & **Overall F1** \\ \hline [PERSON] et al. [17] & - & 92.0 & 82.4 & 77.7 & 80.8 & 77.8 & 86.3 & 88.9 & 84.5 \\ [PERSON] et al. [40] & 91.0 & 90.9 & 62.3 & 80.8 & 69.4 & 76.8 & 71.3 & 84.1 & 84.7 \\ [PERSON] et al. [41] & 92.1 & 93.0 & 74.3 & 79.2 & 81.7 & 86.8 & 77.0 & 89.1 & 88.6 \\ [PERSON] et al. [42] & 93.8 & 94.1 & 75.8 & 83.7 & 83.1 & 80.4 & 87.1 & 92.4 & 90.5 \\ [PERSON] et al. [38] & - & **96.3** & **82.4** & 85.6 & 86.6 & 89.5 & 84.8 & 92.8 & 91.0 \\ [PERSON] et al. [39] & **95.6** & 95.2 & 80.0 & 86.7 & 86.4 & 89.0 & 82.6 & 93.6 & 91.6 \\ \hline
**FP-Deeplab (Ours)** & 94.9 & 96.0 & 81.3 & **87.5** & **88.1** & **89.6** & 84.2 & **94.3** & **92.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with different methods on the HELEN dataset in the overall F1-score.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Baseline** & **CCR-FE** & **SimFA** & **Mean IoU** & **Overall F1** \\ \hline ✓ & & & 78.7 & 89.9 \\ ✓ & ✓ & & 80.2 (+1.5) & 91.1 (+1.2) \\ ✓ & & ✓ & 82.0 (+3.3) & 91.9 (+2.0) \\ ✓ & ✓ & ✓ & **82.5** (+3.8) & **92.2** (+2.3) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study of FP-Deeplab on the HELEN dataset.  
modeling and local detail representation. Finally, when both modules are jointly integrated into the proposed FP-Deeplab network, the model achieves sharper boundary precision while significantly improving the restoration of local facial morphology and the consistency of spatial semantics; this results in clearer structural segmentation contours and more accurate region delineation.  
The consistency between the qualitative and quantitative results demonstrates that FP-Deeplab fully leverages the complementary strengths of the CCR-FE and SimFA modules in spatial structure modeling and channel-wise semantic modulation. This significantly enhances the accuracy and robustness of facial semantic segmentation and offers a practical and effective solution for fine-grained face parsing.