## 5 Conclusions  
Face gender recognition plays a key role in robot-human interactions since it allows robots to adapt their behavior based on the gender of the interacting user, which increases user acceptance and satisfaction. The main goal of the current study was to comprehensively assess the performance of the most successful machine learning model in gender recognition, namely CNN and SVM, when combined with seven common feature extraction methods that included hand-crafted, deep-learned, and fused features. Previous studies on the subject have been mostly restricted to making limited comparisons of hand-crafted and deep-learned features with one model [27; 46] or deep-learned features with multiple models [16; 21]. Furthermore, contradictory findings have been reported about the best-performing combination in the latter category. For this purpose, we performed a comparative analysis of the CNN and SVM models when trained using three hand-crafted features (HOG, LBP, and PCA), deep-learned features (using transfer learning to extract features from a pre-trained VGG-16 model), and a fusion of both features; this analysis yielded seven sets of features. We used the most challenging datasets available, namely, Adience and LFW, and we presented the performance with respect to the accuracy, f-score, and AUC. The most significant findings from this study are that (1) SVM performs the best when trained on a fusion of hand-crafted and deep-learned features, followed by deep-learned \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Features**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{3}{c}{**Datasets**} & \multirow{2}{*}{**Average over All Datasets**} \\ \cline{3-3} \cline{5-6} \multicolumn{2}{c}{} & & & **Adience** & & **LFW** \\ \hline \multirow{3}{*}{Hand-Crafted} & HOG & SVM & 65.6\% & 64.4\% & 65.00\% \\ \cline{2-6}  & LBP & SVM & 62.3\% & 67.3\% & 64.80\% \\ \cline{2-6}  & PCA & SVM & 60.1\% & 65.3\% & 62.70\% \\ \hline \multirow{2}{*}{Deep-Learned} & CNN features & SVM & 83.2\% & 72.6\% & 77.90\% \\ \cline{2-6}  & CNN features & CNN & 89.1\% & 84\% & 86.55\% \\ \hline \multirow{3}{*}{Fusion} & HOG-DL & SVM & 84.1\% & 90.6\% & 87.35\% \\ \cline{2-6}  & & CNN & 82\% & 80.2\% & 81.10\% \\ \cline{2-6}  & & SVM & 84.7\% & 91.3\% & 88.00\% \\ \cline{2-6}  & LBP-DL & CNN & 69.3\% & 89.5\% & 79.40\% \\ \cline{2-6}  & & SVM & 84.6\% & 91.1\% & 87.85\% \\ \cline{2-6}  & PCA-DL & CNN & 51.4\% & 57.2\% & 54.30\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation with respect to AUC on the Adience and LFW datasets. features. The worst performance is exhibited when trained on hand-crafted features. (2) CNN performance decreases when the features are fused with hand-crafted features, including HOG, LBP, and PCA. (3) The CNN model outperforms SVM with all three feature extraction paradigms. The results of this study prove that although deep-learned features can enhance the performance of SVM, CNN still exhibits superior performance in the gender recognition domain. The reported results are possibly influenced by the fact that the Adience dataset is much larger in size than LFW (26,000 vs. 6000) but is more challenging dataset since, unlike LFW, it contains images of individuals from eight age groups [17]. A natural progression of this research would be to analyze the performance using other hand-crafted features, such as SIFT and Gabor filters and with deep-learned features extracted by CNNs of varying architectures and with fine tuning. Another possible area for future research would in investigating whether the findings of this research would hold with cross-data training, where a model is trained on a dataset and tested on a different dataset. Conceptualization, A.A. and H.K. ; Data curation, A.A. and N.A. (Nourah Aloboud); Formal analysis, A.A.; Funding acquisition, H.K. ; Investigation, A.A., N.A. (Nourah Aloboud) and H.K. ; Methodology, A.A. and N.A. (Nourah Aloboud); Resources, H.K. ; Software, N.A. ([PERSON]), F.A. and M.A. ; Supervision, H.K. ; Validation, N.A.