首页|Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
Non-volume preserving-based fusion to group-level emotion recognition on crowd videos
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NSTL
Elsevier
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 ef-fective 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 probabilis-tic model, Non-Volume Preserving Fusion (NVPF), that models spatial information relationships. Further-more, 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 compo-nent 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 peo-ple. Each video is labeled with emotion categories at three levels: individual faces, group of people, and the entire video frame.(c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
