Both social group detection and group emotion recognition in images are growing fields of interest, but never before have they been combined. In this work we aim to detect emotional subgroups in images, which can be of great importance for crowd surveillance or event analysis. To this end, human annotators are instructed to label a set of 171 images, and their recognition strategies are analysed. Three main strategies for labeling images are identified, with each strategy assigning either 1) more weight to emotions (emotion-based fusion), 2) more weight to spatial structures (group-based fusion), or 3) equal weight to both (summation strategy). Based on these strategies, algorithms are developed to automatically recognize emotional subgroups. In particular, K-means and hierarchical clustering are used with location and emotion features derived from a fine-tuned VGG network. Additionally, we experiment with face size and gaze direction as extra input features. The best performance comes from hierarchical clustering with emotion, location and gaze direction as input.