  Group-level emotion recognition (ER) is a growing research area as the
demands for assessing crowds of all sizes are becoming an interest in both the
security arena as well as social media. This work extends the earlier ER
investigations, which focused on either group-level ER on single images or
within a video, by fully investigating group-level expression recognition on
crowd videos. In this paper, we propose an effective deep feature level fusion
mechanism to model the spatial-temporal information in the crowd videos. In our
approach, the fusing process is performed on the deep feature domain by a
generative probabilistic model, Non-Volume Preserving Fusion (NVPF), that
models spatial information relationships. Furthermore, we extend our proposed
spatial NVPF approach to the spatial-temporal NVPF approach to learn the
temporal information between frames. To demonstrate the robustness and
effectiveness of each component in the proposed approach, three experiments
were conducted: (i) evaluation on AffectNet database to benchmark the proposed
EmoNet for recognizing facial expression; (ii) evaluation on EmotiW2018 to
benchmark the proposed deep feature level fusion mechanism NVPF; and, (iii)
examine the proposed TNVPF on an innovative Group-level Emotion on Crowd Videos
(GECV) dataset composed of 627 videos collected from publicly available
sources. GECV dataset is a collection of videos containing crowds of people.
Each video is labeled with emotion categories at three levels: individual
faces, group of people, and the entire video frame.
