# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 1 Introduction  
Emotion recognition is being actively explored in Computer Vision research. With the recent rise and popularization of Machine Learning [1] and Deep Learning [2] techniques, the potential to build intelligent systems that accurately recognize emotions became a closer reality. However, this problem is shown to be more and more complex with the progress of fields that are directly linked with emotion recognition, such as psychology and neurology. Micro-expressions, electroencephalography (EEG) signals, gestures, tone of voice, facial expressions, and surrounding context are some terms that have a powerful impact when identifying emotions in a human [3]. When all of these variables are pieced together with the limitations and problems of the current Computer Vision algorithms, emotion recognition can get highly complex.  
Facial expressions are the main focus of this systematic review. Generally, an FER system consists of the following steps: image acquisition, pre-processing, feature extraction, classification, or regression, as shown in Figure 1.  
Figure 1: Conventional FER system diagram.  
To be able to get a proper facial expression classification, it is highly desirable to provide the most relevant data to the classifier, in the best possible conditions. In order to do that, a conventional FER system will firstly pre-process the input image. One pre-processing step that is common among most reviewed papers is face detection.  
Face detection techniques are able to create bounding boxes that delimit detected faces, which are the desired regions of interest (ROIs) for a conventional FER system. This task is still challenging, and it is not guaranteed that all faces are going to be detected in a given input image. This is especially true when acquiring images from an uncontrolled environment, where there may be movement, harsh lighting conditions, different poses, great distances, among other factors [4].  
When the faces are properly detected, a conventional FER system will process the retrieved ROIs in order to prepare the data that will be fed into the classifier. Normally, this pre-processing step is divided into several substeps, such as intensity normalization for illumination changes, noise filters for image smoothing, data augmentation (DA) [5] to increase the training data, rotation correction for the rotated faces, image resizing for different ROI sizes, image cropping for a better background filtering, among others. After the pre-processing, one can retrieve relevant features from the pre-processed ROIs. There are numerous features that can be selected, such as Actions Units (AUs) [6], motion of certain facial landmarks, distance between facial landmarks, facial texture, gradient features, and so forth. Then, these features are fed into a classifier. Generally, the classifiers used in an FER system are Support Machine Vectors (SVMs) [7] or Convolutional Neural Networks (CNNs) [8].  
This systematic review is organized as follows: Section 2 presents the paper selection criteria for this systematic review; Section 3 presents the most popular FER databases; Section 4 presents the most popular pre-processing methods in FER; Section 5 presents the most popular feature extraction methods in FER; Section 6 presents the most popular classifiers in FER; Section 7 presents the most relevant results obtained by the selected works, as well as a discussion; Section 8 presents insights on the Emotion Recognition in the Wild Challenge (EmotiW); Section 9 presents the conclusion and last remarks of this systematic review.