Abstract : Most techniques for group emotion recognition rely on detection on faces of people and then aggregating the facial information to interpret the group emotion of a given image. However, several faces in the image may be occluded, non-frontal, or indistinguishable, i.e., too many faces in a single image (crowd). This paper focuses on such cases and investigate alternate frameworks which does not involve face detection. The developed frameworks are applied on two datasets - EmotiC and Group Affect Database 3.0 and the results are shown to be competitive with face detection (MTCNN) based approaches. © 2019 IEEE.