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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 5
1585-1593

Visual emotion recognition and prediction based on fusion of background contextual features

Feng Yuehua1,2
Wei Ruoyan1,2
Zhu Xiaoqing3
1. Institute of Management Science of Information Engineering, Hebei University of Economics & Business, Shijiazhuang 050061, China
2. Hebei Cross-Border E-Commerce Technology Innovation Center, Shijiazhuang 050061, China
3. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
DOI: 10.19734/j.issn.1001-3695.2023.08.0388

Abstract

To address the problems of inability to capture the impact of environmental factors and interaction with surrounding individuals on emotion recognition in vision-based affective computing, limitations of describing emotions with a single category, and inability to predict future emotions, this paper proposed a visual emotion recognition and prediction method integrating background context features. This method consisted of an emotion recognition model that integrated background context features(Context-ER) and an emotion prediction model based on GRU and continuous emotion dimensions of Valence-Arousal(GRU mapVA). Context-ER combined facial expressions, body posture, and background context(environment, interaction behavior with surrounding people) features to perform multi-label classification for 26 discrete emotion categories and regression for 3 continuous emotion dimensions. GRU mapVA projected the predicted values of Valence-Arousal onto the improved ValenceArousal model based on the proposed mapping rules, making the differences between sentiment prediction classes more pronounced. Context-ER was tested on the Emotic dataset, and the results show an average precision improvement of over 4% compared to the state-of-the-art methods. GRU-mapVA was tested on three video samples, and the results demonstrate a signi-ficant improvement in emotion prediction compared to existing methods.

Foundation Support

国家自然科学基金资助项目(62103009)
河北省重点研发计划资助项目(17216108)
河北省自然基金资助项目(F2018207038)
河北省高等教育教学改革研究与实践项目(2022GJJG178)
河北省教育厅科研资助项目(QN2020186)
河北经贸大学重点研究项目(ZD20230001)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.08.0388
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 5
Section: Technology of Graphic & Image
Pages: 1585-1593
Serial Number: 1001-3695(2024)05-043-1585-09

Publish History

[2023-11-02] Accepted Paper
[2024-05-05] Printed Article

Cite This Article

冯月华, 魏若岩, 朱晓庆. 融合背景上下文特征的视觉情感识别与预测方法 [J]. 计算机应用研究, 2024, 41 (5): 1585-1593. (Feng Yuehua, Wei Ruoyan, Zhu Xiaoqing. Visual emotion recognition and prediction based on fusion of background contextual features [J]. Application Research of Computers, 2024, 41 (5): 1585-1593. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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