# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 7 Results and Discussion  
In this section, some relevant results from the reviewed papers are presented. This aims to provide insights on what has been done in the FER field based on the reviewed works. However, it is difficult to make a fair comparison between works since most of them used different databases to train and test their FER systems, as well as different ratios of training/test data and different procedures. However, works that followed similar procedures and used the same databases can be compared. There are a few things to be aware of when analyzing the results. There are mainly two testing procedures used in the reviewed works:  
* **Hold-out (HO)** is when the database is split up into two groups: one for training and one for testing. Generally, the training set has more data than the testing set (e.g., 70%/30%). This method has the advantage of being the fastest to be computed; however, it may produce high variance evaluations, since it heavily depends on which data end up being used for training and for testing. * **Cross-validation (CV)**, which can be divided into: 1. \(K\)-fold cross-validation [99], which is when the database is randomly split up into \(k\) groups. One group is for testing and the remaining for training. The process is repeated until every group is used for testing. This method has the advantage of being more robust to the division of the database, since every piece of data ends up being trained \(k\)-1 times and tested once. Therefore, the evaluation variance can be reduced as \(k\) is increased, although the computation time also increases. 2. Leave-\(p\)-out cross-validation [100], which is when all possible sets of \(p\) data are left out from the training and used for validation. Although this method provides more robust evaluations than \(k\)-fold cross-validation, it can become computationally infeasible depending on \(p\). Within these two procedures, there is still the factor of being person-independent (PI): when the same person does not appear in the training set and in the testing set simultaneously. It is relevant to mention that testing procedures which followed a CV and PI approach are the most rigorous ones (meaning they usually have lower accuracy than the other testing procedures). The \"Testing procedure\" column, if it is not PI, means that the procedure potentially carried out with same people in the training set and testing set simultaneously. Another important remark is that some authors had common complaints about FER databases:  
* The posed facial expressions made by actors when building the databases are too artificial. This means that, even if the works present a high accuracy on the benchmarks (using databases that are also built on posed facial expressions), it might not translate into a high accuracy when the same system faces a real world scenario. * Some databases are poorly annotated or have an ambiguous annotation. Authors who noticed this problem tried to overcome it by making their own annotations or by excluding those samples. * Emotion databases are generally small (mainly because of how hard it is to set up the image acquisition system and how hard it is to get several actors to do different facial expressions). It is also worth noting that some classifiers, pre-processing, features, and databases presented in the tables were not approached in the previous sections. Since there is a huge variety of methods and databases used in the reviewed works, it was decided to only mention the most relevant ones. However, if the reader wants to know more about a specific work that used different techniques from the ones mentioned in the previous sections, it is recommended to check their work for a deeper understanding. The reviewed works are orderly referenced to facilitate potential searches. The results are compacted into tables and separated from their different approaches: static image (Table 2), video (Table 3), audiovisual (Table 4), video/static (Table 5) and circumplex model (Table 6).