# Predicting Team Well-Being through Face Video Analysis with AI
## 3 Methods
### Multi-Task Feature Extraction  
After the face of each member is identified, the second step of the proposed FAS, the **multi-task feature extraction**, is employed on the detected faces \(F\) to extract features for the subsequent well-being analysis. Four direct features are extracted. Face emotion recognition (FER) is used to identify and classify human emotions based on facial expressions using the _residual masking network_[37], which performs state-of-the-art analysis on the FER2013 dataset to estimate the six Ekman emotions [10] plus an added \"neutral\" emotion for increased machine learning accuracy. Face alignment is not explicitly employed in this methodology to prevent potential information loss or artifacts. The body landmarks are based on the face-center position while the Gaze estimation evaluates who is looking at whom in a panoramic scene. The approach is based on the 3D head pose and facial landmark estimations to identify where a person is looking. Specifically, _SynergyNet_[38] is used to estimate the full 3D facial geometry. The head poses, and facial landmarks are first spatially transformed to reconstruct the original 3D scene. Then, a visibility algorithm is employed to detect gaze exchanges among individuals. To do so, the human field of view (FOV) angle for 3D gaze pattern estimation has to be set to a specific angle. The number of gaze exchanges is captured in a gaze matrix populated over the duration of the video stream and illustrated in Figure 3.  
Finally, the brightness of the image is extracted directly from the video, reflecting an environmental characteristic. Each team member is assigned the perceived image brightness calculated across all images using the root mean square (RMS) described in Equation (1). It weighs the contributions of the red (\(R\)), green (\(G\)), and blue (\(B\)) channels to take into account the heterogeneity of human perception [39]. \[\mathrm{b}=\sqrt{0.299\cdot(R^{2})+0.587\cdot(G^{2})+0.114\cdot(B^{2})} \tag{1}\]While the face emotion recognition and body landmarks are specific to each individual, the gaze patterns are relative since they result from interactions between team members. Those direct features are used to extract derivative features valuable for the machine learning models and summarized in Table 1.  
The emotion recognition data include details about the emotional and affective states of every team member. The time series for each of [PERSON]'s six basic emotions plus \"neutral\", alongside the distribution of each emotion (_Max Emotion_) and the frequency of changes in emotion (_Freq Emotion changes_), are extracted. The Body Landmarks data provide the position of the head centers of individuals using the standard deviation of the 2D kernel density data distributions in the X and Y directions. They express the spatial extent to which the individual moved during the analyzed video. From these data, the velocity of the head's movement is extracted as a time series by calculating the difference in position between two consecutive frames. Additionally, the presence feature represents the percentage of frames an individual is identified in. The level of brightness is directly extracted from the video as a time series. Finally, the 3D gaze pattern estimation is used to generate interaction matrices and extract social network metrics. The gaze matrix, illustrated in Figure 3, is