## 5 Conclusions  
As mentioned in the introduction to this work, people largely agree on which faces are attractive, both within and across cultures. Our judgments about the attractiveness of others often happen unconsciously and influence us in ways of which we are unaware [55; 56]. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Method** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC (\%) \(\uparrow\)** \\ \hline Alexnet [49] & 0.2651 & 0.3481 & 86.34 \\ Resnet-18 [49] & 0.2419 & 0.3166 & 89.00 \\ ResneXt-50 [49] & 0.2291 & 0.3017 & 89.97 \\ CNN with SCA [50] & 0.2287 & 0.3014 & 90.03 \\ PI-CNN [51] & 0.2267 & 0.3016 & 89.78 \\ CNN + LDL [52] & 0.2201 & 0.2940 & 90.31 \\ ResNet-18 based AaNet [53] & 0.2236 & 0.2954 & 90.55 \\ ResneXt-50-R\({}^{3}\)CNN [54] & 0.2120 & 0.2800 & 91.42 \\ \hline Semi-supervised [29] & 0.2675 & 0.3455 & 86.60 \\ Semi-supervised **(Ours)** & 0.2210 & 0.2870 & 91.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with the state-of-the-art supervised methods using the five-fold cross-validation scenario. Only Reference [29] is a semi-supervised approach. Figure 5: Three prediction examples using two semi-supervised methods. The upper row shows the ground truth scores. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Method** & **MAE \(\downarrow\)** & **RMSE \(\downarrow\)** & **PC (\%) \(\uparrow\)** \\ \hline \multirow{3}{*}{SCUT5500-AF} & VGG-face+fc6 & 0.237 & 0.307 & 89.4 \\  & ResNet-50 & 0.225 & 0.300 & 90.5 \\  & **Proposed method** & 0.220 & 0.277 & 91.3 \\ \hline \multirow{3}{*}{SCUT5500-AM} & VGG-face+fc6 & 0.232 & 0.301 & 89.9 \\  & ResNet-50 & 0.224 & 0.283 & 91.5 \\  & **Proposed method** & 0.218 & 0.276 & 92.2 \\ \hline \multirow{3}{*}{SCUT5500-CF} & VGG-face+fc6 & 0.257 & 0.337 & 88.6 \\  & ResNet-50 & 0.241 & 0.324 & 89.5 \\  & **Proposed method** & 0.231 & 0.302 & 90.3 \\ \hline \multirow{3}{*}{SCUT5500-CM} & VGG-face+fc6 & 0.234 & 0.318 & 88.7 \\  & ResNet-50 & 0.232 & 0.317 & 89.9 \\  & **Proposed method** & 0.230 & 0.300 & 90.5 \\ \hline \multirow{3}{*}{SCUT5500} & VGG-face+fc6 & 0.242 & 0.317 & 89.0 \\  & ResNet-50 & 0.229 & 0.302 & 90.2 \\ \cline{1-1}  & **Proposed method** & 0.221 & 0.2870 & 91.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Semi-supervised face beauty prediction. Psychologists have observed that citizens vote for more attractive political candidates, that judges give more lenient sentences to more attractive defendants and that teachers grade better-looking students more positively [57]. These observations have been difficult to explain; however, today, using novel technologies, such as digital face morphing and brain imaging, psychologists and neuroscientists are beginning to identify the different features that people find attractive in faces as well as the complex networks in the brain that respond to beautiful features. This work not only uncovers neural connections between evaluations of attractiveness and social traits, such as trustworthiness but also offers insights into our appreciation of works of art, such as the [PERSON] bust [58]. In this paper, we presented a general semi-supervised approach to the problem of facial beauty prediction. The main point behind this approach is the use of information available in both the data space and the label space (score space) by using multiple graphs to construct an efficient graph-based representation of the input face images. This graph is used as input to the FME algorithm in an iterative framework where the graph matrix associated with all scores is updated during the optimization process. The experimental results on the dataset SCUT-5500 show that our multimetric fusion method achieved the best performance compared to using a single feature. It also performed well compared to many fully supervised methods (see Table 4). Our main task was to find the best graph that represented face beauty information. In the future, we plan to explore non-linear representations of the data space and incorporate them into the regression model.