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Group-level emotion recognition based on faces, scenes, skeletons features
Author(s): Dejian Li; Ruiming Luo; Shouqian Sun
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Paper Abstract

In this paper, we propose a deep neural network based approach for the group-level emotion recognition in 6th Emotion Recognition in the Wild Challenge (EmotiW 2018). The task of this challenge is to classify a group’s perceived emotion as Positive, Neutral or Negative. Like the most of current researchers on visual emotion recognition, we mainly focus on facial, scene and body clues in images. We treat each clue as mono-model feature and apply early fusion method to combine them together. Experimental results show that our proposed method has outperformed the baseline techniques with the overall test accuracy of 62.90%.

Paper Details

Date Published: 3 January 2020
PDF: 6 pages
Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 1137307 (3 January 2020); doi: 10.1117/12.2557175
Show Author Affiliations
Dejian Li, Zhejiang Univ. (China)
Ruiming Luo, Zhejiang Univ. (China)
Shouqian Sun, Zhejiang Univ. (China)

Published in SPIE Proceedings Vol. 11373:
Eleventh International Conference on Graphics and Image Processing (ICGIP 2019)
Zhigeng Pan; Xun Wang, Editor(s)

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