06 / 2023 – 10 / 2023
Charles Darwin already examined the relation between facial expressions and emotions . In the late 20th century, Paul Ekman studied the cross-cultural perception of emotions and the measurement of facial expressions [2–5]. Various facial coding systems have been invented to describe human feelings, e.g. Monadic Phases Coding System , Maximally Discriminative Facial Movement Coding System  or Facial Animation Parameters . One of the most used is the Facial Action Coding System (FACS), which was originally invented by P. Ekman in 1978  and updated in 2002 . FACS consists of 12 action units (AU) in the upper and 18 in the lower facial area, as well as 14 miscellaneous actions. The system also rates the AUs with different intensities, mostly using a five-point scale (in the letters A-E). In addition, the facial expression can also be divided into temporal segments (onset, apex, offset). Besides frontalview analysis, also profil-view analysis is possible . Although there are many automatic detection algorithms, this work focus on an open source digital biomarker (openDBM) application .
Automatic detection of facial action units found numerous applications over the last few decades [13–18]. What all examples have in common, however, is that the facial expressions were calculated from awake people.
Sigmund Freud already pointed out the relation between sleep and mood . A specific correlation between emotions and sleep architecture has already been shown, for example after watching a stressful movie  or after a stressful day . Lack of sleep, on the other hand, leads to stronger negative emotions during the day [22, 23]. However, studies that extract the facial expressions of sleeping people and examine the connection with the mood the next day were not examined so far.
The goal of this bachelor’s thesis is therefore to implement a pipeline that covers the extraction of facial action units and the computation of facial emotions. For this purpose, a dataset of 25 nights was recorded containing full-night videos from three infrared cameras with respect to different spatial perspectives. Every night was preceded by questionnaires assessing the subjective mood of the participant during three different time points in the day. Analyzing this dataset, the correlation between facial expressions during the night and the mood of the day afterwards, based on patient surveys, will be studied.
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