# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 7 Results and Discussion
\begin{table}
\begin{tabular}{|c c c c c c c|} \hline
**Year** & **Classifier** & **Pre-processing** & **Features** & **Testing Procedure** & **Databases** & **Accuracy** \\ \hline
[102] 2018 & CNN (TL) & DA & ROI & CV (PI) & CK+/SFEW & 98.90\% \\ \hline
[103] 2017 & WMDNN (TL) & Geometric/DA & LBP & CV & CK+/JAFFE/CASIA & 97.02\% \\ \hline
[104] 2018 & DT/NN & Geometric & LBF & CV (PI) & CK+/JAFFE/... & 96.48\% \\ \hline
[105] 2019 & CNN & Smooth & LBP/AUs & CV & CK+/JAFFE & 96.46\% \\ \hline
[106] 2019 & CNN & DA & ROI & HO & FER-2013/JAFFE/... & 96.44\% \\ \hline
[107] 2018 & CNN & Geometric & ROI & HO (PI) & CK+/RaFD/ADFRES & 96.27\% \\ \hline
[108] 2018 & CNN/SVM & - & Gabor & CV & KDEF & 96.26\% \\ \hline
[109] 2017 & IACNN (TL) & Geometric/DRMF & ROI & CV (PI) & CK+/SFEW/MMI & 95.37\% \\ \hline
[110] 2016 & RBF & HE & Curvelet & CV & CK/JAFFE & 95.17\% \\ \hline
[111] 2016 & NN/SVM & HE/Smooth & LBP/Gabor & CV & CK+/MMI & 94.66\% \\ \hline
[112] 2017 & SVM/CRF & - & AAM/Gabor & CV & CK+ & 93.93\% \\ \hline
[113] 2018 & CNN (TL) & Geometric/DA & Facial motions & CV (PI) & CK+/JAFFE/SFEW & 93.64\% \\ \hline
[114] 2018 & RF & - & AUs/HOG & CV (PI) & CK+/BU4D/SFEW & 93.40\% \\ \hline
[115] 2016 & KNN & - & Landmarks & HO & JAFFE/KDEF & 92.29\% \\ \hline
[116] 2008 & ED & - & AAM & CV & FEEDTUM & 91.70\% \\ \hline
[117] 2018 & SVM & CLAHE & DCT & HO & CK+/JAFFE/MMI & 91.10\% \\ \hline
[118] 2019 & SVM & Smooth & CS-LOP & CV (PI) & CK+/JAFFE & 89.58\% \\ \hline
[119] 2019 & CNN & Geometric/DA & ROI/AUs & HO & RaFD/AffectNet & 81.83\% \\ \hline
[120] 2017 & CNN & Geometric/HE & ROI & HO & CK+/JAFFE & 80.30\% \\ \hline
[121] 2016 & SVM & HDR & LBP/SURF & CV (PI) & JAFFE/SFEW & 79.80\% \\ \hline
[122] 2011 & SVM & PCA/Geometric/Smooth & SIFT & CV (PI) & BU-3 DFE & 78.43\% \\ \hline
[123] 2017 & RF & - & Landmarks/HOG & HO & SFEW & 75.39\% \\ \hline
[124] 2019 & DBN/SVM (TL) & - & ROI/OF & CV (PI) & BAUM/RML/MMI & 73.73\% \\ \hline
[125] 2010 & HOSVD & - & Gabor/AUs & CV (PI) & CK/JAFFE & 73.30\% \\ \hline
[126] 2017 & CNN & Geometric/DA & ROI & CV & FER-2013 & 71.03\% \\ \hline
[127] 2015 & CNN (TL) & Geometric/HE & ROI & HO & FER-2013/SFEW & 61.29\% \\ \hline
[128] 2015 & CNN (TL) & Geometric & Landmarks & HO & FER-2013/SFEW & 55.60\% \\ \hline
[129] 2015 & CNN (TL) & Geometric & LBP & HO & SFEW & 54.56\% \\ \hline
[130] 2016 & SVM & - & AAM/AUs & CV & CK+ & 54.47\% \\ \hline
[131] 2015 & CNN (TL) & Geometric & LBP/HOG/... & CV & SFEW & 51.08\% \\ \hline \end{tabular}
\end{table} 
Table 2: Results of reviewed works for static image approaches. Most works tried to analyze facial expressions in a dynamic manner, by using the positional information of the detected facial features. In order to improve results in the video approach, the reviewed works tended to process spatial and temporal information separately. The spatial information can characterize different positions of facial features, while temporal information can capture the flow of these facial features through time (normally from a neutral expression up until a peak facial expression). Afterwards, they aggregated these two types of features and fed them into a single classifier or into a hybrid classifier. Normally, these classifiers need to have the capacity of retaining temporal information, for example by using Long Short-Term Memory Neural Networks (LSTMs) or Recurrent Neural Networks (RNNs). The main conclusion to retrieve from video approaches is that retaining the temporal information usually leads to better results than analyzing each frame separately. However, as in static image approaches, the works with the lowest accuracy rates are from pose-variant databases, which means that retaining the temporal information is still not enough to perform well in uncontrolled environments. Table 4 sums up results of works that approached this problem by analyzing both the video and its audio individually, combining them for the final classification. The columns \"Classifiers\", \"Pre-processing\", and \"Features\" are solely representing the visual analysis.