# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 7 Results and Discussion
However, the \"Accuracy\" column presents the last result of their multimodal system: fusion between video and audio. This approach has similar remarks to the video approach. The main differences are how the reviewed works processed the audio modality and how they combined the audiovisual features for the final model. The fusion methods that were able to attain the best results were the Probability-Based Product Rule and the Bayes sum rule. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Year** & **Classifier** & **Pre-processing** & **Features** & **Testing Procedure** & **Databases** & **Accuracy** \\ \hline
[132] 2016 & ED & Geometric & Landmarks & HO & BU-4 DFE/BP4D-S & 100.00\% \\ \hline
[133] 2017 & GMM & Bandlet & LBP/KW & HO & CK/JAFFE & 99.80\% \\ \hline
[134] 2018 & CNN & HE/Geometric & OF & HO & CK+/SAVEE/AFEW & 98.77\% \\ \hline
[135] 2018 & DFSN-I & Geometric & AUs & CV (PI) & CK+/MMI/CASIA & 98.73\% \\ \hline
[136] 2013 & SVM & - & AAM/AUs & CV & CK+ & 96.80\% \\ \hline
[137] 2017 & DBN & PCA & LDPP & CV & Depth & 96.67\% \\ \hline
[138] 2017 & CNN & PCA & LDSP/LRHP & CV & CK/Bosphorus & 96.25\% \\ \hline
[139] 2016 & ED & - & Landmarks & CV & BU-4 DFE & 96.04\% \\ \hline
[140] 2018 & SVM & - & LBP & CV & CK+/MUG & 95.80\% \\ \hline
[141] 2017 & TFP & LL & LBP & HO & CK/JAFFE & 94.84\% \\ \hline
[142] 2017 & HMM & - & LBP/POMF & HO & Depth & 94.17\% \\ \hline
[143] 2012 & SVM/RBF & - & OF/HOG & CV (PI) & CK & 87.44\% \\ \hline
[144] 2007 & SVM & - & AAM/AUs & CV & CK & 85.00\% \\ \hline
[145] 2013 & HMM & Geometric & Landmarks & CV (PI) & BU-4 DFE & 79.40\% \\ \hline
[146] 2006 & RNN & - & FAPs & HO & SAL & 79.00\% \\ \hline
[147] 2011 & SVM & - & LBP/MHH & HO & GEMEP & 70.30\% \\ \hline
[148] 2007 & Bayes & Intensity adjustment & Landmarks & CV (PI) & CK & 70.20\% \\ \hline
[149] 2015 & SVM & - & LBP\_TOP & CV (PI) & CASME II & 69.63\% \\ \hline
[150] 2019 & CNN/LSTM (TL) & Geometric & ROI/OF & CV (PI) & RML/eNTERFACE’05 & 65.72\% \\ \hline
[151] 2016 & CNN/SVM & PCA/Geometric & LBP\_TOP/SIFT & HO & AFEW & 40.13\% \\ \hline
[152] 2013 & SVM & PCA & LBP/Gabor & HO & AFEW & 30.05\% \\ \hline
[153] 2013 & SVM & Geometric & Gabor/AUs & HO & AFEW & 29.81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of reviewed works for video approaches. As in static image and video approaches, works with the lowest accuracy rates mostly resulted from uncontrolled environment databases (FER-2013 and AFEW). However, their results concluded that a multimodal approach is generally better than a unimodal approach. Table 5 sums up results of a work that approached this problem by analyzing both the video and their frames individually, combining them for the final classification. The main remarks made by the authors is that this combined approach leads to better results than analyzing both parts alone. Table 6 sums up results of works that approached this problem by analyzing the circumplex model. What is presented in the \"Accuracy\" column is an average of the calculated metrics in this model. One thing that one can immediately notice is that every work used a video approach. The main features used for this approach revolve around facial landmarks and LBP features. As for the classification step, the main classifiers revolve around LSTMs, SVMs, HMMs, KNNs, and Support Vector Regressions (SVR). As in the video approach, in order to improve the results, the reviewed works tried to track facial features over time, processing their spatial and temporal information separately. By using a hybrid classifier with the capacity of retaining temporal information (LSTM), with a proper pre-processing step (e.g., intensity normalization which was neglected in the reviewed works of this table), it is possible to achieve competitive results when determining the metrics of the