# FaceReader Insights into the Emotional Response of Douro Wines
## 2 Materials and Methods
### Data Analysis by FaceReader  
The collected results were analyzed using FaceReader software, version 9.0 (Noldus Information Technology, Wageningen, The Netherlands). For video analysis, the basic version of the program was utilized, employing the default face model and a sample rate set to capture every frame. Subsequently, an Excel file containing quantified emotions was generated.  
Data analysis involved three stages, following the methodology described by [PERSON] et al. (2015). Firstly, face detection proceeded to model facial expressions and concluded with classifying expressions using an artificial neural network. This classification process allowed the quantification of basic emotions, including happy, sad, angry, surprised, scary, and disgusted. The quantification of these emotions ranged between 0 (indicating no detection of the emotion) and 1 (representing maximum detection). FaceReader has been trained using intensity values annotated by human experts. A mixture of emotions often causes facial expressions; two (or more) expressions may coincide with high intensity. Therefore, the sum of the intensity values for the expressions at a particular time point usually does not equal 1.  
For some people, FaceReader can be biased toward certain expressions. So, to correct person-specific biases, participant calibration was performed using images in which the participant looks neutral. The calibration procedure uses the image or frame with the lowest model error and expressions other than neutral found in this image for calibration.  
FaceReader also assesses facial arousal, showing whether the test participant is active (+1) or inactive (0). This arousal is determined by evaluating the activity of 20 action units (AUs) from the facial action coding system (FACS) (2016).  
The calculation of arousal involves using the activation values (AV) of 20 specific action units (AUs) as input. These include AU 1 (inner brow raiser), AU 2 (outer brow raiser), AU 4 (brow however), AU 5 (upper lid raiser), AU 6 (cheek raiser), AU 7 (lid tightener), AU 9 (nose wrinkler), AU 10 (upper lip raiser), AU 12 (lip corner puller), AU 14 (dimpler), AU 15 (lip corner depressor), AU 17 (chin raiser), AU 20 (lip stretcher), AU 23 (lip part), AU 24 (lip pressor), AU 25 (lips part), AU 26 (jaw drop), and AU 27 (mouth stretch), along with the inverse of AU 43 (eyes closed). AU 43 is inverted because closed eyes generally indicate low arousal, contrasting with the other AUs that typically signify high arousal. The average AU activation values (AAV) are calculated over the tasting. During the initial seconds of the analysis, the AAV is based on the data available up to that point, where the AAV is computed as the mean of the AV over the preceding seconds. These average values are then subtracted from the current AU activation values to obtain the corrected activation values (CAV). This step adjusts for consistently activated AUs, which might be attributed to an individual's inherent expression tendencies. The CAV is determined as follows: \(CAV=max(0,AV-AAV)\). Finally, arousal is measured by averaging the five highest CAV, resulting in _arousal = mean_ (of the top 5 CAV) (2016). Figure 1 shows a FaceReader screenshot recording the emotion \"surprised\".