# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 3 Experiments
### Mixture of CNN and SVM Classifier for CK+ Database  
For the mixture of the facial expression classifiers using a weighted summation of the probabilistic estimation of the facial expression categories, it is necessary to determine \begin{table}
\begin{tabular}{c c} \hline
**Input Data Type of SVM** & **Recognition Performance** \\ \hline Landmark point & 91.74 \\ \hline Landmark point (normalized) & 92.97 \\ \hline Intensity, angle of landmark flow & 94.80 \\ \hline Landmark displacement of landmark flow & 96.64 \\ \hline \end{tabular}
\end{table} 
Table 6: SVM-based facial expression recognition performance in different feature data types of facial geometry. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **An** & **Co** & **Di** & **Fe** & **Ha** & **Sa** & **Su** \\ \hline
**An** & 100.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline
**Co** & 0.0 & 100.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline
**Di** & 1.7 & 0.0 & 98.3 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline
**Fe** & 4.0 & 0.0 & 0.0 & 92.0 & 4.0 & 0.0 & 0.0 \\ \hline
**Ha** & 4.3 & 0.0 & 0.0 & 1.4 & 92.6 & 0.0 & 1.5 \\ \hline
**Sa** & 3.6 & 0.0 & 0.0 & 0.0 & 3.6 & 92.8 & 0.0 \\ \hline
**Su** & 0.0 & 0.0 & 0.0 & 0.0 & 1.2 & 0.0 & 98.8 \\ \hline \end{tabular}
\end{table} 
Table 7: Confusion matrix: SVM-based facial displacement (CK+) from displacement, intensity and angle. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **An** & **Co** & **Di** & **Fe** & **Ha** & **Sa** & **Su** \\ \hline
**An** & 95.56 & 0.0 & 2.2 & 0.0 & 2.2 & 0.0 & 0.0 \\ \hline
**Co** & 5.55 & 88.9 & 0.0 & 0.0 & 5.55 & 0.0 & 0.0 \\ \hline
**Di** & 5.1 & 0.0 & 93.2 & 0.0 & 1.7 & 0.0 & 0.0 \\ \hline
**Fe** & 4.0 & 0.0 & 0.0 & 92.0 & 4.0 & 0.0 & 0.0 \\ \hline
**Ha** & 2.9 & 0.0 & 0.0 & 0.0 & 97.1 & 0.0 & 0.0 \\ \hline
**Sa** & 7.1 & 0.0 & 0.0 & 0.0 & 3.6 & 89.3 & 0.0 \\ \hline
**Su** & 1.2 & 0.0 & 0.0 & 0.0 & 1.2 & 0.0 & 97.6 \\ \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix: SVM-based facial displacement (CK+) from the landmark displacement. Using the mixture of the modified CNN-based classifier and SVM-based classifier, we achieved 99.69% facial expression recognition performance, which is higher than that of the mixture of the basic CNN-based classifier and SVM-based classifier. The confusion matrix in the case of the fusion of the modified CNN-based classifier and SVM classifier is shown in Table 9.  
We compared the performance of the CK+ database with other approaches in the literature. First, we compared the proposed method with AU-Aware Deep Networks (AUDN), in which the researchers proposed a deep learning method for facial expression recognition by incorporating the existing knowledge that the appearance variations caused by expressions can be decomposed into a batch of local facial action units (AUs) [47]. Second, we compared the proposed method with occlusion-robust expression prediction using local subspace random forest (WLS-RF) [50], in which the researchers train random forests upon spatially defined local subspaces of the face. Third, we compared the proposed scheme with the appearance features using salient facial patches [52], in which the researchers suggested a novel foundation for expression identification by extracting a few facial patches, depending upon the location of facial markers. Fourth, we compared the proposed scheme with the joint model of the deep temporal appearance model and deep temporal geometry model (DTAGN) [38], which is stationed on two diverse frameworks. The framework evokes temporal appearance features from the image pattern and temporal geometry features from temporal facial marker spots. Finally, we compared the proposed method with CNN with SIFT (Scale Invariant Feature Transform) [48], in which the researchers suggested CNN for face recognition and attained very good accuracy by using a small portion of training data. Table 10 shows the facial expression recognition performance in different approaches for the CK+ database.