## 4 Experimental Results
### Performance Evaluation and Discussion
#### 4.3.3 Performance Evaluation on MEBeauty  
The anticipated presence of a substantial number of outliers in the MEBeauty dataset is attributed to the non-normal distribution of its data. Therefore, adopting CNNs with loss ensembles is necessary to obtain superior results over conventional regression-loss methods. Table 8 displays the results of five-fold cross-validation conducted on three pretrained CNNs, utilizing various loss functions for the MEBeauty dataset. It was revealed that L1 and L2 loss functions exhibit competitive results within AlexNet. However, for both VGG16 and FIAC-Net, L1 loss produces slightly lower PC values and higher error rates compared to L2 loss. In contrast, the Log-cosh loss function consistently yields improved results compared to both L1 and L2 loss functions.  
Upon integration of the proposed ensemble loss function with AlexNet, the outcome consisted of a PC value of 0.8976712, MAE of 0.476394, and RMSE of 0.6097078. In contrast, VGG16-Net exhibited a slightly improved PC value of 0.907883. However, it also exhibited a slightly higher error rate, recording values of 0.512113 and 0.636318 in terms of MAE and RMSE, respectively, when compared to AlexNet.  
Regarding FIAC-Net performance, it works well with a PC value of 0.925977, MAE of 0.426317, and RMSE of 0.536646. The reason behind the superiority of the fine-tuned FIAC-Net is that it was previously well-trained in [41] on more than 200,000 challenging facial images to efficiently classify the attractiveness of facial images. Meanwhile, AlexNet and VGG16-Net were pretrained on the vast ImageNet dataset for an object-classification task. Figure 9 illustrates the effectiveness of the proposed fine-tuned FIAC-Net with ensemble loss functions in predicting the beauty scores of MEBeauty-tested data. Meanwhile, Figure 10 presents a visual representation that facilitates a direct comparison between the estimated values and the true values of utilized CNNs that were implemented on the MEBeauty dataset.  
The MEBeauty dataset presents a challenging and diverse collection of facial images captured under unconstrained conditions. It distinguishes itself through its larger scale in comparison to the SCUT-FBP dataset, which focuses exclusively on frontal images of Asian females, while remaining smaller than the SCUT-FBP5500 dataset, which encompasses frontal images of both Asian and Caucasian individuals. It is crucial to acknowledge that the size of a dataset alone does not guarantee improved model performance. Additional factors,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**AlexNet**} & \multicolumn{4}{c}{**VGG16-Net**} & \multicolumn{2}{c}{**FIAC-NET**} \\ \hline
**Loss** & **Fold \#** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC \(\uparrow\)** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** \\ \hline \multirow{10}{*}{L2} & Fold1 & 0.907345 & 0.240533 & 0.307498 & 0.923767 & 0.213235 & 0.272778 & 0.921218 & 0.208841 & 0.267048 \\  & Fold2 & 0.907436 & 0.284204 & 0.35930  
such as gender, facial expressions, and poses of the subjects, significantly influence the model's efficacy. By accounting for these factors, researchers can obtain valuable insights into the intricate trade-off between image quality, dataset size, and their profound impact on the model's findings and outcomes. In this study, the proposed model demonstrated its effectiveness through efficient pretraining on the CelebA dataset for facial-attractiveness classification.  
Figure 10: A scatter-plot representation of samples of predicted beauty scores against actual beauty scores on MEBeauty: (**a**) AleqNet, **(b)** VGG16-Net, and (c) FIAC-Net.