## 5 Experiments
### Ablation Studies  
Our proposed method comprises two primary components: a training strategy leveraging 3D labels and a local feature extraction module utilizing a Swin Transformer [86]. To determine the effectiveness of each component, we conducted several ablation studies. We employed the \(L_{con}\) loss function throughout these experiments and adhered to the same hyperparameters outlined in Section 5.1. Our baseline models used ResNet18 and ResNet50 [84] with pre-trained weights from ImageNet [85]. Furthermore, we evaluated the performance of our method on the PE3D benchmark to illustrate its effectiveness. Effectiveness of 3D labels in PE3D: The results presented in Table 9 highlight the efficacy of incorporating 3D labels in the training process. Our experiment involved two backbone models operated under the same conditions. Notably, without including PE3D 3D labels, which contain 3D emotion information during training, ResNet18 and ResNet50 achieved accuracies of only 57.2% and 62.3% on the PE3D testing dataset, respectively. However, training with \(Loss_{dna}\) that integrated 3D emotion centers' information resulted in a notable increase in accuracy by over 2%. This finding highlights the pivotal role of the PE3D dataset in boosting the models' proficiency in recognizing variations in portrait facial emotions. Additionally, we evaluated a method, SCN [49], devoid of training with \(Loss_{dna}\). In contrast, the approach referenced in [52], which solely leverages 3D labels and utilizes Fuzzy C-means [89] for classification, was also examined. According to Table 9, the integration of 3D labels surpassed the deep learning technique by an excess of 15% regarding accuracy on the PE3D test set. This result reaffirms the substantial impact of 3D labels in emotion estimation, particularly in datasets characterized by considerable variation, such as PE3D. Effectiveness of PST network: To evaluate the effectiveness of our proposed approach, Portrait Swin Transformation (PST), along with its components, was conducted using several experiments on the dataset. An analysis of Table 10 demonstrates that integrating global and local features enhances the base model's performance by an additional 2-5%. Furthermore, the study also explored the application of 3D labels. The results indicate that adding 3D labels to PST does not significantly improve accuracy during testing (around 1-2%). We believe the testing accuracy is determined using a single categorical emotion label. The 3D labels, in contrast, are more suited for depicting complex emotions, as illustrated in the examples in Figure 9. Conversely, PST is more capable of learning from the training set by extracting pertinent features for emotion classification. Comparison with the state-of-the-art: Table 11 displays the accuracy performance of the PST and PE3D labels on the FER+ [40] dataset. The combined PE3D + PST method was benchmarked against contemporary leading-edge FER techniques, with the evaluation focusing on categorical accuracy metrics. Our methodology demonstrates an average improvement of 1% over the baseline model, surpassing other current models in performance. These results indicate that our approach is superior in facial emotion classification. Consequently, our approach not only boosts the performance of existing models but also significantly enhances their robustness. \begin{table}
\begin{tabular}{l l c} \hline \hline
**Methods** & **ST Module** & **Loss Functions** & **PE3D Testing** \\ \hline \hline \multirow{3}{*}{ResNet18 [84]} & N & \(Loss_{ce}\) & 57.2 \\  & Y & \(Loss_{ce}\) & **63** \\  & Y + 3D & \(Loss_{ce}+Loss_{dua}\) & 62.8 \\ \hline \multirow{3}{*}{ResNet50 [84]} & N & \(Loss_{ce}\) & 64.9 \\  & Y & \(Loss_{ce}\) & **67** \\  & Y + 3D & \(Loss_{ce}+Loss_{dua}\) & 65.1 \\ \hline \hline \end{tabular}  
* Highest accuracy is highlighted in bold.  
\end{table} 
Table 10: Evaluation of global and local features on the fused dataset.