# Emotion Detection Using Facial Expression Involving Occlusions and Tilt
## 3 Materials and Methods  
In this section, the proposed approach is presented. The mandate of the proposed approach is to consider the challenges like occlusions, illumination variations, and changes in the physical appearance of mute persons' images and mitigate their effects. The model is designed to identify the basic expressions of human emotions such as joy, grief, surprise, fear, contempt, anger, and neutral. The proposed model is based on 6 layers of CNN, in which 5 convolutional layers are used, including the max-pooling layer and one dense layer with a dropout function. Figure 1 provides the flow of the proposed model, where preprocessing of the obtained image set is undertaken in the first step followed by face detection and cropping in the second step. In the third step, the image is flipped vertically, and 2 images and 7 angles from each image are formed producing a total of 14 images in the final step. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & \begin{tabular}{c} **Face** \\ **Detection** \\ \end{tabular} & **Preprocessing** & \begin{tabular}{c} **Feature** \\ **Extraction** \\ \end{tabular} & **Classification** & **Database** & **Emotions** & **Result** \\ \hline \multirow{3}{*}{[31]} & Haar & \multirow{3}{*}{-} & Directional & \multirow{3}{*}{Multiclass SVM (DTP)} & \multirow{3}{*}{JAFFE CK+} & \multirow{3}{*}{7} & 85\% (JAFFE), 96\% (CK+) \\  & Cascades & & & & & (CK+) \\ \hline \multirow{3}{*}{[15]} & \multirow{3}{*}{-} & LDA and PCA for & \multirow{3}{*}{\
\begin{tabular}{c} Landmarks LBP \\ \end{tabular} & \multirow{3}{*}{SVM Multi-Class} & \multirow{3}{*}{JAFFE CK+} & \multirow{3}{*}{6} & 94.39\% (CK+) and 92.22\% (JAFFE) \\ \hline \multirow{3}{*}{[28]} & Haar & \multirow{3}{*}{Cropping} & \multirow{3}{*}{AAM} & \multirow{3}{*}{SVM RBF} & LFW, FDDB, and YFD & \multirow{3}{*}{-} & 89\%–100\% w.r.t datasets \\  & feature-based & & & & & \\ classifier & & & & & & \\ training and & & & & & & & \\ LBP and Haar & & & & & & & \\ cascades & testing phase & & & & & & \\ \hline \multirow{3}{*}{[10]} & Viola–Jones & Gaussian Kernel (while & \multirow{3}{*}{PHOG + LBP acquisition)} & \multirow{3}{*}{SVM (Multi-class)} & \multirow{3}{*}{CK+ JAFFE} & \multirow{3}{*}{6} & (CK+) 93.63\%, (JAFFE) 83.86\% \\  & Haar & & & & & & \\ Cascades & & & & & & \\ classifier & & & & & & \\ \hline \multirow{3}{*}{[32]} & \multirow{3}{*}{-} & Weighted Least & Gabor + log, & \multirow{3}{*}{SVM} & Self-defined & \multirow{3}{*}{6} & (Log Gabor) \\  & square (WLC) & Gabor, PCA for & feature reduction & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{[33]} & \multirow{3}{*}{-} & \multirow{3}{*}{Normalization} & \multirow{3}{*}{GLTP and DGLP} & SVM (one vs & \multirow{3}{*}{CK} & \multirow{3}{*}{7} & \multirow{3}{*}{77\%} \\  & & & other) RBF & & & & \\ \hline \multirow{3}{*}{[34]} & \multirow{3}{*}{-} & PCA used for & \multirow{3}{*}{ULBP, EOG, LPG, FFF(83P), FD} & \multirow{3}{*}{SVM multi-class} & \multirow{3}{*}{BU-3 DFE} & \multirow{3}{*}{7} & \multirow{3}{*}{79.46\%} \\  & & dimensionality reduction after & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{[35]} & \multirow{3}{*}{-} & PCA used for & \multirow{3}{*}{
\begin{tabular}{c} High \\ \end{tabular} & SVM (LIBSVM) & SFEW & \multirow{3}{*}{7} & \multirow{3}{*}{35.96\%} \\  & & reduction after & & & & \\ extraction & & & & & & \\ \hline \multirow{3}{*}{[36]} & \multirow{3}{*}{-} & \multirow{3}{*}{-} & Viola–Jones detector & \multirow{3}{*}{LIBSVM} & RAF-DB & \multirow{3}{*}{5} & \multirow{3}{*}{-} \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & Viola–Jones & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Furthermore, in the proposed framework, the first convolutional layer uses a 5 \(\times\) 5 filter. It takes a 32 \(\times\) 32 sized image of grayscale which means the number of channels is 1. Its output size is 32 feature maps.