## 4 Proposed Work
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**WAO-CM**} & \multicolumn{4}{c}{**LHCMA**} & \multicolumn{4}{c}{**SHHSA**} \\ \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Image Size**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} \\ \cline{3-3} \cline{5-10}  & & & & & & & & & & \\ \hline \multirow{4}{*}{JAFFE} & \(32\times 32\) & 0.808 & 0.810 & 0.809 & 0.864 & 0.870 & 0.868 & **0.875** & **0.879** & **0.876** \\  & \(48\times 48\) & 0.886 & 0.882 & 0.883 & 0.898 & 0.896 & 0.897 & **0.929** & **0.922** & **0.924** \\  & \(64\times 64\) & 0.817 & 0.816 & 0.835 & 0.831 & 0.832 & 0.863 & **0.857** & **0.859** \\  & \(32\times 32\) & 0.865 & 0.869 & 0.868 & **0.926** & **0.924** & **0.924** & 0.994 & 0.891 & 0.892 \\ \cline{1-1}  & \(48\times 48\) & 0.938 & 0.935 & 0.937 & **0.970** & **0.972** & **0.971** & 0.960 & 0.954 & 0.956 \\ \cline{1-1}  & \(64\times 64\) & 0.953 & 0.950 & 0.951 & **0.973** & **0.976** & **0.974** & 0.961 & 0.963 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance of SFHSA with respect to WAO-CM and LHCMA for HOG features in terms of Precision, Recall and F-measure. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{} & \multicolumn{4}{c}{**WAO-CM**} & \multicolumn{4}{c}{**LHCMA**} & \multicolumn{4}{c}{**SHHSA**} \\ \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Image Size**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} \\ \cline{3-3} \cline{5-10}  & & \multirow{2}{*}{**Image Size**} & & & & & & & & \\ \hline \multirow{4}{*}{JAFFE} & \(32\times 32\) & 0.609 & 0.610 & 0.609 & **0.725** & **0.727** & **0.727** & 0.680 & 0.674 & 0.675 \\  & \(48\times 48\) & 0.523 & 0.520 & 0.521 & **0.635** & **0.636** & **0.635** & 0.561 & 0.558 & 0.559 \\  & \(64\times 64\) & 0.548 &

## 6 Conclusions

In this paper, we have focused our attention in reducing the dimension of the feature sets obtained from facial expression images extracted using five feature descriptors--uLBP, hvnLBP, Gabor filters, HOG and PHOG. The evaluation of the proposed methodology, called SFHSA, is done on two benchmark FER datasets, namely RaFD and JAFFE. The proposed algorithm was applied on the extracted feature sets and reduced-feature subsets were obtained with higher accuracy score. It is

Figure 8: Graphical representation of the dimensions and accuracy scores (%) of the reduced-feature subsets in HM for PHOG-based feature vector extracted from images of dimension \(32\times 32\) in RaFD dataset, where distinguishable colors denote different feature dimensions of the reduced-feature subsets. Figure 7: Graphical representation of the dimensions and accuracy scores (%) of the reduced-feature subsets in HM for PHOG-based feature vector extracted from images of dimension \(32\times 32\) in the JAFFE dataset. Distinguishable colors denote different feature dimensions of the reduced-feature subsets. evident from the results that our FS technique has effectively filtered out redundant/irrelevant features and also outperformed many existing FS techniques like SA, GA and MA. Following the primary backbone of traditional HSA, we have proposed filter variant of the HSA. Cosine similarity was used for the purpose of adjustment of features. On the other hand, both the mRMR and PCC values were used as a way for determining the feasibility of the optimal feature subsets. The performance of the SFHSA was evaluated using SMO classifier.