# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 3 Experiments
### Facial Expression Recognition for B14D Database  
We have also evaluated the facial expression recognition performance using the BU4D database [67]. BU4D is a high-resolution 3D dynamic facial expression database, which contains facial expression sequences captured from 101 subjects. For each subject, there are \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Details** & **Recognition Performance** \\ \hline [PERSON] et al. [47] & Using DNN & 91.74 \\ \hline [PERSON] et al. [50] & Using WLS-RF & 92.97 \\ \hline [PERSON] et al. [52] & Using salient facial patches & 94.09 \\ \hline [PERSON] et al. [38] & Using DTAN + DTGN & 96.64 \\ \hline [PERSON] et al. [48] & Using CNN + SIFT & 99.10 \\ \hline Proposed method & Using modified CNN + SVM & 99.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Facial expression recognition performance in different approaches for CK+ database. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \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 & 94.4 & 0.0 & 0.0 & 5.6 & 0.0 & 0.0 \\ \hline
**Di** & 0.0 & 0.0 & 100.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline
**Fe** & 0.0 & 0.0 & 0.0 & 100.0 & 0.0 & 0.0 & 0.0 \\ \hline
**Ha** & 0.0 & 0.0 & 0.0 & 0.0 & 100.0 & 0.0 & 0.0 \\ \hline
**Sa** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 100.0 & 0.0 \\ \hline
**Su** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Confusion matrix for CK+ database: Weighted summation of modified CNN-based and SVM-based classifiers. six model sequences showing six prototypic facial expressions (anger, disgust, happiness, fear, sadness and surprise), respectively. Each expression sequence contains approximately 100 frames. Figure 9 shows a sample sequence of a surprise expressions and its landmark points in the BU4D database. Depth data and texture data are provided. We used only texture data for the analysis of facial expressions. Using the mixture of the modified CNN-based classifier and SVM-based classifier, we achieved 94.69% facial expression recognition performance using four-fold cross-validation. Table 11 shows the confusion matrix of facial expression recognition for the BU4D database using the proposed method. We also compared the facial expression recognition performance with other facial expression recognition systems using the BU4D database. First, we compared the proposed method with the bags of motion word (BoMW) model [44], which finds facial expressions by using local facial motion descriptors and motion words from clustering its descriptors. Second, we compared the proposed method with the 3D facial surface descriptor, spatial HMM and temporal HMM (2D-HMM) model used for facial expression recognition from 3D facial surfaces [51]. The method uses three classes of HMMs: temporal 1D-HMM, pseudo 2D-HMM (a fusion of a spatial HMM and a temporal HMM) and real 2D-HMM. Third, we compared the proposed method with the Local Binary Pattern Three Orthogonal Plane (LBP-TOP), which is used to extract the spatiotemporal features of facial expressions [53]. The method examines two space-time detectors and four descriptors and uses the bag of features framework for human facial expression recognition. Fourth, we compared the proposed method with 3D motion-based features captured using Free-Form Deformations (FFDs) [49], in which a revealing pattern is geared to contain an onset followed by an apex and an offset. Fifth, we have also compared the proposed method with the Dynamic Geometric Image Network (DGIN), which uses different geometric quantities for dynamic 3D facial expression recognition [45]. A two-stage long-term and short-term sliding window scheme is presented for data augmentation and temporal pooling throughout the training phase.