2.3. Spontaneous

Spontaneous emotion datasets are considered to be the closest to actual real-life scenarios. However, since true emotion can only be observed, when the person is not aware of being recorded [30], they are difficult to collect and label. The acquisition of data is usually in conflict with privacy or ethics, whereas the labelling has to be done manually and the true emotion has to be guessed by the analyser [25]. This arduous task is both time-consuming and erroneous [13, 38], having a sharp contrast with posed and induced datasets, where labels are either predefined or can be derived from the elicitation content.

With that being said, there still exist a few databases out there that consist of data extracted from movies [39, 40], YouTube videos [41], or even television series [42], but these databases have inherently fewer samples in them than their posed and induced counterparts. Example images from these databases are in Figures 3–5 respectively.

3. Categories of emotion

Figure 5. TV show stills taken from the VAM database [43].

The purpose of a database is defined by the emotions represented in it. Several databases like CK [27, 44], MMI [45], eNTERFACE [46], NVIE [47] all opt to capture the six basic emotion types: anger, disgust, fear, happiness, sadness and surprise as proposed by Ekman [48–50]. In the tables, they are denoted as primary 6. Often authors tend to add contempt to these, forming seven primary emotions and often neutral is included. However, they cover a very small subcategory of all possible emotions, so there have been attempts to combine them [51, 52].

Figure 4. Spanish YouTube video clips taken from the Spanish Multimodal Opinion database [41].

Review on Emotion Recognition Databases http://dx.doi.org/10.5772/intechopen.72748 43

Several databases try to just categorise the general positive and negative emotions or incorporate them along with others, e.g. the SMO [41], AAI [36], and ISL meeting corpus [35] data-

Apart from anger and disgust within the six primary emotions, scientists have tried to capture other negative expressions, such as boredom, disinterest, pain, embarrassment and depression.

TUM AVIC [53] and AVDLC [12] databases are amongst those that try to label levels of interest and depression while GEMEP [31] and VAM [43] attempt to divide emotions into four quadrants and three dimensions, respectively. The main reason why most databases have a very small number of categories (mainly, neutral and smile/no-smile) is that the more emotions added, the

bases. Some even try to rank deception and honesty like the CSC corpus database [37].

more difficult they are to label and also more data is required to properly train a model.

Unfortunately, these categories are harder to elicit than other types of emotions.

Figure 3. Images of movie clips taken from the AFEW database [39, 40].

Figure 4. Spanish YouTube video clips taken from the Spanish Multimodal Opinion database [41].

Figure 5. TV show stills taken from the VAM database [43].
