## 6 Conclusions  
This research examined the classification accuracy and computation time for facial emotional expressions with a) handcrafted feature extraction methods LBP and HOG and b) CNN-based feature extraction. KDEF, RaFD, and JAFFE databases have been used. The use of neural networks was two-fold. Initially, the features were exported without retraining the networks to the new data, from 25%, 50%, 75%, and 100% of their depths. Extracting features from shallower layers is significantly more efficient if the new images are different from those in which the networks were trained initially (as has been the case in this work). The second use of neural networks was to extract features after retraining them in the new data (transfer learning method).  
Table 9 summarizes the results of the three methods used for the three databases. Regarding the handcrafted methods, LBP gives higher success rates on the high-resolution images of the large databases (KDEF and RaFD), while, on the contrary, HOG on the lower-resolution images of the straight pose of JAFFE. Classification results appear improved by directly extracting features from shallow layers of residual architecture networks. In addition, we observe a reduction in computational time for the large databases compared to the handcrafted methods. Finally, the transfer learning method enhances the classification accuracy for large databases, significantly impacting computational time. The classification accuracy was not improved in JAFFE (as it was a smaller set), with the highest classification rate remaining at 92.86%. The SVM classifier performs better than the inbuilt CNNs classifier.  
According to these findings we could build a decision framework to support the appropriate choice based on the specifications of each application. Such a framework is presented in Table 10.  
Overall, we could say that the handcrafted features implemented for decades do not reach the performance of neural networks. The golden mean between classification performance and computational time is the simplest and fastest method of passing images  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ResNet18**} & \multicolumn{2}{c}{**ResNet50**} & \multicolumn{2}{c}{**ResNet101**} & \multicolumn{2}{c}{**Inception\_v3**} & \multicolumn{2}{c}{**EfficientNet-B0**} \\ \hline  & **Salt and** & **Gaussian** & **Salt and** & **Gaussian** & **Salt and** & **Gaussian** & **Salt and** & **Gaussian** & **Salt and** & **Pepper** & **Gaussian** \\  & **Pepper** & **Gaussian** & **Pepper** & **Gaussian** & **Pepper** & **Gaussian** & **Pepper** & **Gaussian** \\ \hline KDEF & 5.46 & 8.47 & 4.28 & 7.12 & 5.30 & 8.65 & 10.27 & 15.51 & 27.75 & 41.91 \\ JAFFE & 27.03 & 37.84 & 23.08 & 33.34 & 23.08 & 33.34 & 49.99 & 52.77 & 69.70 & 75.75 \\ RaFD & 2.59 & 3.94 & 1.78 & 2.92 & 3.83 & 4.65 & 3.90 & 4.86 & 12.91 & 19.91 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Percentage deviation of classification accuracy with corrupted test images for each CNN per database.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Salt and Pepper Noise** & **Gaussian Noise** \\ \hline KDEF & 55.22 & 67.72 \\ JAFFE & 55.55 & 66.67 \\ RaFD & 80.50 & 82.48 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Percentage deviation of classification accuracy for each database, with corrupted test files, for the handcrafted methods.  
through CNNs and extracting their features from intermediate layers. If the application requirements need the highest possible classification rate, then a large number of images is necessary to retrain networks. Among the architectures examined, the Residual Networks proved to be the more efficient. The effect of noise is more destructive in handcrafted methods than in CNNs. Of the latter, ResNet50 proved to be the most robust in each case.