## 2 Literature Review
The studies [33] and [34] investigated the use of deep-learned features with the CNN and SVM models; they report contradictory findings. While the result in [33] proved that the fine-tuned model outperformed SVM, when oversampling was applied, the best performance was achieved in [34] when the deep-learned features were extracted from a fine-tuned VGG model and fed into an SVM model. [PERSON] et al. [32] showed that feeding hand-crafted features to CNN can improve their performance. The SVM performances with hand-crafted features and CNNs with deep-learned features are explored in [21; 37; 38; 81]; CNNs with deep-learned features achieved the best results. Studies in the field of gender recognition have only focused on comparing the two feature extraction paradigms with one model [32], a single paradigm with multiple models [33; 34], or limited feature extraction methods and models [21; 37; 38; 81]. Because of the variations in the experimental setups, the results from different studies cannot be compared. Therefore, it is not clear yet how the different feature extraction paradigms (i.e., hand-crafted, deep-learned, and fused features) would perform when combined with different models (including CNN and SVM); this concern is addressed in this research. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Feature Descriptor** & **Classifier** & **Dataset** & **Result (Accuracy)** \\ \hline \multirow{8}{*}{[21]} & Image pixels & Support vector machine (SVM) & FERET & 78.65\% \\ \cline{3-5}  & & WWW & 76.71\% \\ \cline{3-5}  & Image pixels & Neural network (NN) & FERET & 86.98\% \\ \cline{2-5}  & Local binary patterns (LBP) & SVM & FERET & 81.40\% \\ \cline{3-5}  & Deep neural network (DNN) & DNN & LFW & 92.60\% \\ \cline{3-5}  & Deep convolutional neural network (DCNN) & DCNN & LFW & 94.09\% \\ \cline{3-5}  & & Gallagher & 86.04\% \\ \cline{3-5}  & Local-DNN & Local-DNN & LFW & 96.25\% \\ \cline{3-5}  & Histogram of oriented gradients (HOG) & & [PERSON] & 90.58\% \\ \cline{3-5}  & Principal component analysis (PCA) & & & 88.23\% \\ \cline{3-5}  & LBP & & & 77.91\% \\ \cline{3-5}  & Local oriented statistics information booster (LOSIB) & SVM & GROUPS & 86.74\% \\ \cline{3-5}  & Local salient patterns (LSP) & & & 86.65\% \\ \cline{3-5}  & HOG + LBP + LOSIB & & & 85.58\% \\ \cline{3-5}  & CNN + HOG + LBP + LOSIB & & & 94.28\% \\ \hline \multirow{3}{*}{[32]} & [PERSON] response & CNN & & 97.23\% \\ \cline{3-5}  & & & & \multicolumn{1}{c}{} \\ \cline{3-5}  & Fused Gabor response & CNN & & 99.20\% \\ \cline{3-5}  & Convolutional neural network (CNN) & CNN & & 99.10\% \\ \cline{3-5}  & CNN & CNN & & 99.10\% \\ \hline \multirow{3}{*}{[33]} & CNN & SVM & & 99.10\% \\ \cline{3-5}  & CNN & SVM & & 99.10\% \\ \cline{3-5}  & CNN & SVM & & 99.10\% \\ \cline{3-5}  & CNN & SVM & & 99.10\% \\ \cline{3-5}  & CNN & SVM & & 99.10\% \\ \cline{3-5}  & PCA & SVM & & 99.10\% \\ \cline{3-5}  & Image pixels & SVM & & 99.10\% \\ \cline{3-5}  & HOG & SVM & & 99.10\% \\ \cline{3-5}  & Double tree complex wavelet transform (DTCWT) & SVM & & 99.10\% \\ \hline \multirow{3}{*}{[41]} & CNN & CNN & & 99.10\% \\ \cline{3-5}  & CNN & CNN & & 99.10\% \\ \cline{3-5}  & CNN & CNN & & 99.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related works with their reported results.