# GCE: An Audio-Visual Dataset for Group Cohesion and Emotion Analysis
## 2 Related Works  
The group cohesion and group emotion datasets can be divided into image-based and video-based datasets, as shown in Tables 1 and 2. The HAPPEI dataset [13] was curated by [PERSON] et al. in 2015 [14] and comprises 2638 images. The dataset underwent meticulous annotation by four human labelers, characterizing the happiness intensity across six distinct stages (0-5): neutral, small smile, large smile, small laugh, large laugh, and thrilled. The MultiEmoVA dataset [11] comprises online photos that were hand-gathered and categorized to identify the prevailing emotions depicted in a group photograph. The GAF-Cohesion [15] image dataset was created as a follow-up to the original GAF3.0 dataset. Each image in the GAF-cohesion was tagged with information on the estimated group mood and cohesion score. The GroupEmoW [14] is a recent image dataset used for evaluating group emotion. It was acquired from Google, Baidu, Bing, and Flickr using terms connected to social occasions, such as burials, birthdays, protests, conferences, meetings, weddings, etc. For video-based datasets, a new crowd dataset, labeled with one of five fine-grained aberrant behavior categories (panic, fight, congestion, obstruction, and neutral) was suggested in 2016 by [PERSON] et al. [16]. The SALSA dataset was developed by [PERSON] et al. [17] \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Purpose** & **Scenario** & **Modality** &
\begin{tabular}{c} **Data** \\ **Size** \\ \end{tabular} & **Duration** & **Annotator** & **Annotation** \\ \hline \multirow{4}{*}{HAPPEI [13]} & Identify the visual & & & & & Neutral, Small \\  & cues for group & In the wild & Image & 3134 & - & 4 & smile, Large smile, \\  & happiness stages & & & & & Small laugh, Large \\  & Identify the arousal & & & & & & laugh, Thrilled \\ MultiEmoVA [11] & and valence expressed & In the wild & Image & 250 & - & 15 & Arousal, Valence \\  & at the group-level & & & & & & Strongly agree, \\  & Identify the visual & & & & & & Strongly disagree \\  & cues for group & In the wild & Image & 14,175 & - & 5 & Agree, Disagree, \\  & cohesion scores & & & & & & Strongly disagree \\ GroupEmoW [14] & Identify group-level & In the wild & Image & 15,894 & - & 5 & Valence states \\  & emotion in the wild & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent datasets of group cohesion and group emotion analysis using images. to analyze spontaneous chat rooms. Videos covering a wide spectrum of emotions and themes (such as interviews, festivals, parties, quiet protests, acts of violence, etc.) were compiled in the VGAF (Video Group AFfect) dataset [12]. The videos were cut into smaller segments of between 8 and 25 s long. To explore the modeling of the collaborative features of multimodal behavior in groups that accomplish basic tasks, the MULTISIMO ([[https://multisimo.eu/datasets.html](https://multisimo.eu/datasets.html)]([https://multisimo.eu/datasets.html](https://multisimo.eu/datasets.html)), accessed on 1 May 2024) [18] multimodal corpus was established in 2018. We have become aware of specific constraints within the latest datasets that are accessible for scholarly investigation in the fields of group communication, group cohesion, and emotional states. It is worth mentioning that a considerable number of these datasets have been obtained in controlled settings, such as the MULTISIMO and SALSA datasets. In addition, many of these datasets primarily consist of picture data, as demonstrated by the MultiEmoVA, GAF-Cohesion, and GroupEmoW datasets. However, many datasets, such as VGAF, primarily focus on evaluating the cohesiveness and emotional state of crowds in non-verbal situations, which may inadvertently mask the individual dynamics within these groupings. Based on these findings, we introduce a new video dataset designed to explore group communication, cohesion, and emotional dynamics comprehensively.