Non-volume preserving-based fusion to group-level emotion recognition on crowd videos. (August 2022)

• Record Type: Journal Article
• Authors: Quach, Kha Gia; Le, Ngan; Duong, Chi Nhan; Jalata, Ibsa; Roy, Kaushik; Luu, Khoa
• Is Part Of: Pattern recognition. Volume 128(2022)
• Publication Date: 2022-08
• Subjects: Group-level emotion recognition -- Facial features -- Feature extraction -- Feature fusion -- Crowd videos; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108646
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Estimating group-level emotions on crowd videos using fused facial features. An effective facial expression network to extract facial expression features. Deep feature-level fusion mechanism combining spatial and temporal features. Collecting a large video database for group-level emotion on crowd videos. Abstract: 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.