CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术研究
Dominant and Complementary Emotion Recognition from Still Images of Faces
Jianzhu Guo; Zhen Lei; Jun Wan
Source PublicationIEEE Access
AbstractEmotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g. happily-disgusted, sadly-fearful), which is more detailed than the seven classical facial emotions (e.g. happy, disgust, etc.). Current studies on compound emotions are limited to use datasets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED dataset, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED dataset, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed dataset. Experiments indicate that pairs of compound emotion (e.g. surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared to the seven basic emotions. However, we hope the proposed dataset can help to pave the way for further research on compound facial emotion recognition.
KeywordDominant And Complementary Emotion Recognition Compound Emotions Fine-grained Face Emotion Dataset
Indexed BySCI
WOS IDWOS:000434935200001
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
AffiliationCenter for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jianzhu Guo,Zhen Lei,Jun Wan. Dominant and Complementary Emotion Recognition from Still Images of Faces[J]. IEEE Access,2018,6(1):2169-3536.
APA Jianzhu Guo,Zhen Lei,&Jun Wan.(2018).Dominant and Complementary Emotion Recognition from Still Images of Faces.IEEE Access,6(1),2169-3536.
MLA Jianzhu Guo,et al."Dominant and Complementary Emotion Recognition from Still Images of Faces".IEEE Access 6.1(2018):2169-3536.
Files in This Item: Download All
File Name/Size DocType Version Access License
Access2018-emotion.p(14104KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jianzhu Guo]'s Articles
[Zhen Lei]'s Articles
[Jun Wan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jianzhu Guo]'s Articles
[Zhen Lei]'s Articles
[Jun Wan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jianzhu Guo]'s Articles
[Zhen Lei]'s Articles
[Jun Wan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Access2018-emotion.pdf
Format: Adobe PDF
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.