## 4 Proposed Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Feature** **Descriptor**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Dimension of** **Facial Images**} & \multirow{2}{*}{**Size of the** **Original-Feature Vector**} & \multicolumn{2}{c}{**Size of the** **Accuracy**} & \multicolumn{2}{c}{**Size of the Optimal**} & \multirow{2}{*}{**Accuracy Score**} \\  & & & **Facial Images** & & **Coore (\%)** & **Obtained by SFHSA** & (\%)**[Change in \%]** \\  & & & & & **[Reduction in \%]** & & \\ \hline \multirow{4}{*}{uLBP} & JAFFE & \(32\times 32\) & & 62.34 & 570[60.38\%] & 78.95[\(+\)16.61\%] \\  & JAFFE & \(48\times 48\) & & 62.34 & 339[35.91\%] & 75.32[\(+\)12.98\%] \\  & & \(64\times 64\) & & 59.74 & 541[57.31\%] & 74.13[\(+\)14.39\%] \\ \cline{2-8}  & & \(32\times 32\) & & 83.58 & 608[64.14\%] & 87.75[\(+\)14.71\%] \\  & RaFD & \(48\times 48\) & & 88.62 & 445[47.14\%] & 85.48[\(-\)3.14\%] \\  & & \(64\times 64\) & & 86.38 & 573[60.70\%] & 92.16[\(+\)5.78\%] \\ \hline \multirow{4}{*}{hvnLBP} & JAFFE & \(32\times 32\) & & 57.14 & 1380[33.69\%] & 67.41[\(+\)10.27\%] \\  & JAFFE & \(48\times 48\) & & 46.75 & 1416[34.57\%] & 55.84[\(+\)12.09\%] \\  & & \(64\times 64\) & 4096 & 44.16 & 1433[34.99\%] & 67.42[\(+\)23.26\%] \\ \cline{2-8}  & & \(32\times 32\) & & 66.42 & 1513[36.94\%] & 72.39[\(+\)5.97\%] \\  & RaFD & \(48\times 48\) & & 74.07 & 1493[36.45\%] & 75.74[\(+\)1.37\%] \\  & & \(64\times 64\) & & 69.4 & 1494[34.64\%] & 75.81[\(+\)6.41\%] \\ \hline \multirow{4}{*}{Gabor} & JAFFE & \(32\times 32\) & 640 & 67.53 & 197[30.78\%] & 81.82[\(+\)14.29\%] \\  & JAFFE & \(48\times 48\) & 1440 & 72.73 & 560[38.89\%] & 92.91[\(+\)19.48\%] \\  & & \(64\times 64\) & 2560 & 71.43 & 818[31.95\%] & 90.91[\(+\)19.48\%] \\ \cline{2-8}  & & \(32\times 32\) & 640 & 90.49 & 241[37.66\%] & 91.91[\(+\)1.42\%] \\  & RaFD & \(48\times 48\) & 1440 & 95.71 & 3412[32.68\%] & 96.51[\(+\)0.89\%] \\  & & \(64\times 64\) & 2560 & 98.51 & 462[18.04\%] & 97.02[\(-\)0.72\%] \\ \hline \multirow{4}{*}{HOG} & JAFFE & \(32\times 32\) & 324 & 71.43 & 189[58.33\%] & 87.94[\(+\)16.51\%] \\  & JAFFE & \(48\times 48\) & 900 & 74.03 & 403[34.78\%] & 92.21[\(+\)18.18\%] \\  & & \(64\times 64\) & 1764 & 71.43 & 1411[79.99\%] & 85.71[\(+\)14.28\%] \\ \cline{2-8}  & & \(32\times 32\) & 324 & 88.43 & 2053[23.72\%] & 89.15[\(+\)0.72\%] \\  & RaFD & \(48\times 48\) & 900 & 94.22 & 385[42.78\%] & 95.40[\(+\)1.18\%] \\  & & \(64\times 64\) & 1764 & 93.66 & 544[30.83\%] & 96.32[\(+\)2.66\%] \\ \hline \multirow{4}{*}{PHOG} & JAFFE & \(32\times 32\) & & 53.25 & 321[47.21\%] & 76.32[\(+\)20.07\%] \\  & JAFFE & \(48\times 48\) & & 66.23 & 408[60.00\%] & 85.27[\(+\)19.04\%] \\ \cline{1-1}  & & \(64\times 64\) & 680 & 59.74 & 409[60.15\%] & 84.39[\(+\)24.65\%] \\ \cline{1-1} \cline{2-8}  & & \(32\times 32\) & & 78.54 & 429[63.09\%] & 85.01[\(+\)1.70\%] \\ \cline{1-1} \cline{2-8}  & RaFD & \(48\times 48\) & & 85.45 & 271[39.85\%] & 89.03[\(+\)3.58\%] \\ \cline{1-1} \cline{2-8}  & & \(64\times 64\) & & 88.81 & 300[44.12\%] & 88.30[\(-\)0.51\%] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Tabular representation of the details of original-feature size, reduced-feature size, accuracy score and their corresponding percentage of reduced-feature size with respect to original-feature size and change in accuracy score of 5 different feature vectors extracted from facial images of 3 different dimensions for two popular FER datasets. The initial value of HM consists of HMS (=15), which is randomly generated feature subsets having dimensions ranging from 80% to 90% of the original-feature vector. The detail evaluation on the reduced-feature subsets obtained as a result of using SFHSA on uLBP, hvnLBP, Gabor-filter-based, HOG, PHOG feature descriptors and their corresponding accuracy score were presented in Tables 3-7 respectively. We have used the sequential minimal optimization (SMO) classifier with linear kernel [33] to evaluate the recognition performance on the reduced-feature sets.