# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 7 Results and Discussion
In the \"Pre-processing\" column, face detection is not present since every work did this ROI extraction technique. In Tables 2-5, the last column shows the accuracy obtained for each work in predicting emotions. Most works considered the six basic emotions, plus the neutral expression. However, not every work tried to make predictions in these six basic emotions, plus the neutral expression: some used five out of the six basic emotions, some added one more (contempt, usually seen on the CK+ database), and others went further and connected them (e.g., happily surprised). In Table 6, the last column shows the accuracy obtained for each work in predicting emotions using the circumplex model. This model has a wide variety of metrics, such as valence, arousal, power, dominance, expectation, and activation. Depending on the value of these metrics, one can predict which emotion certain subject is feeling. Figure 22 illustrates this concept using the valence and arousal metrics. Tables 2-6 present the results of the reviewed works during this systematic review, sorted by accuracy. Some works tested their algorithms using various databases, but only the best classification result is presented in the tables. Table 2 sums up results of works that approached this problem by analyzing static images. It is interesting to notice that the top results in this table mostly use CNNs with or without variants, CNNs combined with SVMs, and the top two did TL from pre-trained models. The works that attained the best results generally used a CNN pre-trained on large databases, being fine-tuned with smaller databases afterwards. This shows why CNNs have been dominating the FER field (as well as numerous Computer Vision fields): the joint optimization of both feature extraction and classification. Some used an end-to-end CNN approach, which is only fed with ROIs (faces) that could have been pre-processed or not. In other words, this approach does not need to choose which features to use for training their CNN: the CNN selects the features to learn by itself during the training step. Figure 22: The 2D valence-arousal emotion space [101]. Other works used classifiers like DTs, RFs, or SVMs, instead of conventionally trying to overcome the problems of using CNN classifiers in FER (such as overfitting because of the overall small databases). These works showed that, with good understanding of the problem and of the used databases, as well as a proper pre-processing step (such as DA and intensity/spatial normalization), feature selection (such as AUs, LBP, HOG or Gabor features) and fine-tuning, it is possible to achieve competitive results. However, there is a common discrepancy of accuracy when testing in controlled environment databases and in wild environment databases. This shows a clear difficulty in translating the good results in controlled environments (such as CK+ and JAFFE) to uncontrolled environments (such as FER-2013 and SFEW). Every work that tested its algorithms with various databases obtained a significantly worse result on the uncontrolled environment ones. One example from this table is the work [102], which despite obtaining 98.90% accuracy when testing on the CK+ database, only obtained 55.27% accuracy on the SFEW database. This is mainly caused by the head pose variation and the different lighting conditions to which a real world scenario is susceptible. Table 3 sums up results of works that approached this problem by analyzing videos. It is possible to see the introduction of temporal features like OF and Motion History Histograms (MHHs). The pre-processing step revolves around intensity/spatial normalization and PCA. As for the classification step, there is a wide variety of classifiers that revolves around EDs, Gaussian Mixture Model (GMM), CNNs, SVMs and HMMs.