## 4 Experimentation
### Results and Discussion
#### 4.2.5 Comparison  
To compare the performance of the proposed methodology, we computed features from the competing methods and obtained the performance under the same training-testing protocol. Here, the performance of methods of Vgg16 [67], ResNet50 [68], that of [PERSON] et al. [42], Inception-v3 [69], and that of [PERSON] et al. [70] were compared with the performance of the proposed system for the KDEF database, as presented in Table 7. For the GENKI database the performance of the proposed system was compared with Vgg16, ResNet50, Inception-v3, that of [PERSON] et al. [29], that of [PERSON] et al. [71], and that of [PERSON] et al. [72], and the competing methods are presented in Table 8. Similarly, we compared the performance of the proposed system with that of [PERSON] et al. [73], ResNet50, and Inception-v3 for the CK+ database in Table 9. For the SFEW database, the performance of [PERSON] et al. [74], Vgg16, ResNet50, and the Inception-v3 methods were compared in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **KDEF** & **GENKI** \\ \hline \(CNN_{1}\) & 75.95 & 92.45 \\ \hline \(CNN_{2}\) & 78.18 & 94.13 \\ \hline \(CNN_{3}\) & 80.92 & 95.59 \\ \hline _Sum-Rule_ & 81.53 & 96.03 \\ \hline _Product-Rule_ & **82.63** & **96.75** \\ \hline
**Method** & **CK+** & **SFEW** \\ \hline \(CNN_{1}\) & 95.89 & 34.05 \\ \hline \(CNN_{2}\) & 96.22 & 34.91 \\ \hline \(CNN_{3}\) & 96.71 & 35.72 \\ \hline _Sum-Rule_ & 97.07 & 36.15 \\ \hline _Product-Rule_ & **97.32** & **36.79** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of score-level fusion approaches on the performance of the proposed FERS.  
Figure 14: Effect of transfer learning on the performance of the proposed FERS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Remarks** \\ \hline Vgg16 [67] & 65.08 & Images used (980), expression class (7), train/test split \\ \hline ResNet50 [68] & 72.32 & Images used (980), expression class (7), train/test split \\ \hline [PERSON] et al. [42] & 72.55 & Images used (980), expression class (7) \\  & & Images type (frontal), 10-fold cross validation \\ \hline Inception-v3 [69] & 75.04 & Images used (980), expression class (7), train/test split \\ \hline [PERSON] et al. [70] & 74.05 & Images used (720), expression class (6) \\  & & Images type (frontal), 10-fold cross validation \\ \hline
**Proposed** & **82.63** & Images used (980), proposed CNN for seven expression classes \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of the proposed FERS for the KDEF database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy (\%)** & **Remarks** \\ \hline Vgg16 [67] & 72.08 & VGG16 CNN for seven expression classes \\ \hline ResNet50 [68] & 82.30 & ResNet 50 CNN for seven expression classes \\ \hline Inception-v3 [69] & 85.38 & Inception-v3 CNN for seven expression classes \\ \hline [PERSON] et al. [29] & 88.50 & Feature (HOG), classifier (ELM) \\ \hline [PERSON] et al. [71] & 94.21 & Feature (CNN), classifier (Softmax) \\ \hline [PERSON] et al. [72] & 94.33 & Feature (ensemble), classifier (ensemble) \\ \hline Proposed & **96.75** & Proposed CNN for seven expression classes \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance comparison of the proposed FERS for the GENKI database.  
Figure 15: Confusion matrix for the (**a**) KDEF, (**b**) GENKI, (**c**) CK+, and (**d**) SFEW dataset after the product-rule-based fusion performance of the proposed FERS.