# Guided Spatial Transformers for Facial Expression Recognition
## 2 Related Work
### Emotion Recognition  
Emotions are considered a psychological state. Due to their interest and variety of applications, they have been vastly studied from different scopes as psychology, computer science, medicine, etc.  
Regarding psychological and neuroscience studies, they usually focus on discovering when a person manifests a specific emotion or how the brain generates the emotions. As an attempt to answer these questions, many psychological theories were proposed.  
Two of the most relevant psychological theories that deepen the categorization of emotions were proposed by [PERSON] in 1999 [19], and by [PERSON] and [PERSON] in 2005 [20].  
[PERSON]'s theory [19] establishes that there are six main families of universal emotions. These families of emotions are anger, fear, sadness, enjoyment, disgust, and surprise. Later, [PERSON] and [PERSON] developed a new theory: \"the circumplex model of affect\". This new theory contrasts with the [PERSON] assumptions as they considered that emotions appear as a product of two independent neural systems, one that informs about the valence (how positive or negative is an emotion) and the other that represents the arousal (how intense or soft is an emotion) [20].  
These psychological theories built the base to create most of the current existing datasets for emotion recognition [11; 12; 21; 22; 23; 24]. Depending on the corpus, emotions are annotated in terms of a group of families, following the [PERSON]'s theory [12; 21]; in terms of valence and arousal [24], as [PERSON] and [PERSON] said; or following both strategies [11; 22; 23].  
The apparition of these datasets incentivized the investigation of new features and models to learn to recognize these emotions. An example of features is the landmarks estimation, which consists of extracting the coordinates of some key points around the eye-brows, eyes, nose, mouth, and jaw. Works such as that in [25] demonstrate that landmarks encapsulate important information about the facial expression of a person. Another example of features highly related to emotions are the Facial Action Units (FACs). In [26], they extract several FACs and introduce them into a 7-layer autoencoder, recognizing emotions with a high accuracy rate. One of the advantages of FACs compared with landmarks is that they encapsulate a facial movement, with information being more highly processed. However, it is not easy to create a mask from the FACs information, whereas landmark coordinates can be easily represented in an image.  
Regarding the models, some of the most interesting for this work are Emotional-DAN [27] and Deep-Emotion [5]. EmotionalDAN can solve emotion, valence, and landmarks recognition at once, improving its baseline model that did not include the landmarks and valence predictors. It is interesting to mention this model because it also uses landmarks to improve the final results. On the other hand, Deep-Emotion uses an STN architecture to address emotion recognition, emphasizing that these models are appropriate to solve our task.  
The last mention is for the study of [PERSON], [PERSON] et al. [28]. Among their experiments, they evaluate the results of multiplying the original input image with a salience map. When they introduce this combined image into their model instead of the original image, the network learns from the filtered image. This result indicates that saliency can localize the most relevant areas in an image, allowing the network to extract emotional information.  
Our work extends the previous studies by suggesting and comparing several methods to improve the performance of the localization network in STNs for Facial Emotion Recognition tasks.