## 4 Results and Discussion  
The experimental results are shown in Tables 2-4 for both Adience and LFW datasets. The tables show the performance of CNN and SVM models with different types of features. We trained SVM with seven types of features, namely, HOG, LBP, PCA, deep-learned, fusion of HOG and deep-learned, fusion of LBP and deep-learned, and fusion of PCA and deep-learned features. Moreover, we trained CNN with four features, namely, deep-learned, fusion of HOG and deep-learned, fusion of LBP and deep-learned, and fusion of PCA and deep-learned features. The parameters of the methods were instantiated based on the empirical experiments and by following the recommendations from the literature. All the reported results are the average of five-fold cross validation. T-tests were used to analyze the relationship between the performances of different combinations of features and classifiers. Table 2 is quite revealing in several ways. First, we can observe that, on average, SVM performs comparably with HOG and LBP features, whereas it has slightly less accuracy using the PCA features. Yet, when deep-learned features are used, SVM performance with respect to the accuracy increases by 12.95% as compared with the best performance with hand-crafted features. However, what is interesting in our result is that the best SVM performance is achieved when fused features are used because the classifier achieves at  
least 22.40% and 9.45% increase in accuracy as compared with hand-crafted and deep-learned features, respectively. Our SVM results with deep-learned features outperform those reported in [33] when SVM with dropout and oversampling is trained on the Adience dataset. Next, we considered the CNN model. We observed that the CNN model had the best performance with deep-learned features. Table 2 shows that the model accuracy is 86.60% with deep-learned features; however, this accuracy drops by at least 5.65% when fused features are used. These results contradict earlier findings by [32], which showed that feeding hand-crafted features to CNN can improve their performance. This difference can be explained by the fact that only Gabor filters were used in [32] as hand-crafted features. Furthermore, the CNN accuracy achieved in this research is higher than that reported in [41], where a CNN model trained on the Adience dataset achieved 84% accuracy. On comparing the SVM and CNN performances with different types of features, we can see that the CNN model with deep-learned features outperforms the best SVM result with fused features on the Adience dataset. However, opposite results were obtained on the LFW dataset. Our T-test shows that the result with the Adience dataset (\(p=0.0002\)) is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{4}{c}{**Datasets**} \\ \cline{3-6}  & & & **Adience** & **LFW** & **Average over All Datasets** \\ \hline \multirow{3}{*}{Hand-Crafted} & HOG & SVM & 65.5\% & 64.4\% & 64.95\% \\ \cline{2-6}  & LBP & SVM & 62.5\% & 67.3\% & 64.90\% \\ \cline{2-6}  & PCA & SVM & 60.9\% & 65\% & 62.95\% \\ \hline \multirow{3}{*}{Deep-Learned} & CNN features & SVM & 83.3\% & 72.5\% & 77.90\% \\ \cline{2-6}  & CNN features & CNN & 89.2\% & 84\% & 86.60\% \\ \hline \multirow{3}{*}{Fusion} & \multirow{3}{*}{HOG-DL} & SVM & 84.1\% & 90.6\% & 87.35\% \\ \cline{2-6}  & & CNN & 81.7\% & 80.2\% & 80.95\% \\ \cline{2-6}  & & SVM & 84.9\% & 91.3\% & 88.10\% \\ \cline{2-6}  & LBP-DL & CNN & 71.4\% & 89.7\% & 80.55\% \\ \cline{2-6}  & PCA-DL & SVM & 84.8\% & 91.1\% & 87.95\% \\ \cline{2-6}  & PCA-DL & CNN & 54.3\% & 57.2\% & 55.75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation with respect to accuracy on the Adience and LFW datasets.