4. Database types

Emotion recognition databases may come in many different forms, depending on how the data was collected. We review existing databases for different types of emotion recognition. In order to better compare similar types of databases, we decided to split them into three broad categories based on format. The first two categories separated still images from video sequences, while the last category is comprised of databases with more unique capturing methods.

#### 4.1. Static databases

Most early facial expression databases, like the CK [27], only consist of frontal portrait images taken with simple RGB cameras. Newer databases try to design collection methods that incorporate data, which is closer to real life scenarios by using different angles and occlusion (hats, glasses, etc.). Great examples are the MMI [45] and Multi-PIE [72] databases, which were some of the first well-known ones using multiple view angles. In order to increase the accuracy of the human expression analysis models, databases like the FABO [22] have expanded the frame from a portrait to the entire upper body.

Static databases are the oldest and most common type. Therefore, it's understandable that they were created with the most diverse of goals, varying from expression perception [29] to neuropsychological research [73], and have a wide range of data gathering styles, including selfphotography through a semi-reflective mirror [74] and occlusion and light angle variation [75]. Static databases usually have the largest number of participants and a bigger sample size. While it is relatively easy to find a database suited for the task at hand, categories of emotions are quite limited, as static databases only focus on six primary emotions or smile/neutral detection. In the future, it would be convenient if there were databases with more emotions, especially spontaneous or induced, because, as you can see in Table 2, almost all static databases to date are posed.
