## 4 Experiments
### Results on CelebAMask-HQ
#### 4.3.1 Performance Comparison with State-of-the-Art Methods  
We compared our method with several representative state-of-the-art approaches, including [PERSON] et al. [14], [PERSON] et al. [33], [PERSON] et al. [39], [PERSON] et al. [18], and [PERSON] [43]. These methods are characterized by their modeling strategies for complex facial regions and demonstrate strengths in boundary refinement, context modeling, and semantic enhancement. Under the same evaluation metrics and testing protocol, we assessed the segmentation performance of all methods on the CelebAMask-HQ test set. As shown in Table 3, the proposed FP-Deeplab achieves superior performance across multiple key facial components, particularly in structurally complex regions such as hair, lips, and nose. Moreover, for long-tailed categories, such as \"necklace\" and \"clothes\", which appear less frequently in the dataset, FP-Deeplab also demonstrates excellent performance, highlighting its strong generalization ability under class-imbalanced semantic distributions. Figure 5 presents several visualized segmentation results of our proposed FP-Deeplab on the CelebAMask-HQ test set. To intuitively evaluate the model's ability to parse different facial components, we selected multiple challenging samples, including cases with occluded facial parts and complex hair textures. As shown in the figure, FP-Deeplab accurately identifies and segments fine-grained structures, such as eyebrows, eyes, and lips, and also maintains good shape consistency and semantic integrity in smaller regions like hair boundaries, mouth contours, and earrings. Additionally, the model demonstrates smooth and continuous predictions in boundary transition areas and shows stable performance on long-tailed categories such as earrings, necklaces, and clothing, reflecting its strong structural perception and semantic discrimination capabilities. Furthermore, the precise restoration of complex facial structures further indicates that FP-Deeplab excels in fine-detail modeling, boundary preservation, and feature fusion. Compared to several advanced methods listed in Table 3 that rely on large-scale pretraining (e.g., FaRL), FP-Deeplab achieves similarly robust performance on multiple key regions and long-tailed classes, even without the aid of large-scale pretrained models. These results not only highlight the model's competitiveness in quantitative evaluation metrics but also emphasize its practicality and robustness in real-world applications. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Face** & **Nose** & **Classes** & **L-Eye** & **R-Eye** & **L-Brown** & **R-Brown** & **L-Ear** & **R-Ear** & **R-Ear** \\  & **I-Mouth** & **U-Lip** & **L-Lip** & **Hair** & **Hat** & **Earring** & **Necklace** & **Neck** & **Cloth** & **Mean** \\ \[PERSON] et al. [14] & 94.8 & 90.3 & 75.8 & 79.9 & 80.1 & 77.3 & 78.0 & 75.6 & 73.1 & 76.2 \\  & 89.8 & 87.1 & 88.8 & 90.4 & 58.2 & 65.7 & 19.4 & 82.7 & 64.2 & 76.2 \\ \[PERSON] et al. [33] & 95.5 & 85.6 & 92.9 & 84.3 & 85.2 & 81.4 & 81.2 & 84.9 & 83.1 & \multirow{2}{*}{80.3} \\  & 63.4 & 88.9 & 90.1 & 86.6 & **91.3** & 63.2 & 26.1 & **92.8** & 68.3 & \\ \[PERSON] et al. [39] & 96.4 & 91.9 & 89.5 & 87.1 & 85.0 & 80.8 & 82.5 & 84.1 & 83.3 & 82.1 \\  & 90.6 & 87.9 & 91.0 & 91.1 & 83.9 & 65.4 & 17.8 & 88.1 & 80.6 & \\ \[PERSON] et al. [18] & 96.0 & 93.7 & 90.6 & 86.2 & 86.5 & 83.2 & 83.1 & 86.5 & 84.1 & \multirow{2}{*}{84.0} \\  & **93.8** & 88.6 & 90.3 & 93.9 & 85.9 & **67.8** & 30.1 & 88.8 & 83.5 & \\ \hline \multirow{2}{*}{FaRL\({}_{\text{scratch}}\)[43]} & 96.2 & 93.8 & 92.3 & **89.0** & **89.0** & **85.3** & **85.4** & **86.9** & **87.3** & \multirow{2}{*}{84.7} \\  & 91.7 & 88.1 & 90.0 & 94.9 & 82.7 & 63.1 & 33.5 & 90.8 & 85.9 & \\ \hline \multirow{2}{*}{FP-Deeplab (Ours)} & **96.4** & **94.0** & **94.2** & 82.2 & 82.5 & 79.4 & 79.0 & 82.2 & 80.6 & \multirow{2}{*}{**84.8**} \\  & 93.2 & **90.2** & **91.6** & **95.7** & 85.8 & 59.6 & **59.7** & 92.0 & **88.3** & \\ \hline \hline \end{tabular}
\end{table}