Knowledge Commons of Institute of Automation,CAS
Expression Analysis Based on Face Regions in Real-world Conditions | |
Zheng Lian1,2; Ya Li1; Jian-Hua Tao1,2,3; Jian Huang1,2; Ming-Yue Niu1,2 | |
发表期刊 | International Journal of Automation and Computing |
ISSN | 1476-8186 |
2020 | |
卷号 | 17期号:1页码:96-107 |
摘要 | Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full occlusions. Those challenges lead to different face areas with different degrees of sharpness and completeness. Inspired by this fact, we focus on the authenticity of predictions generated by different pairs. For example, if only the mouth areas are available and the emotion classifier predicts happiness, then there is a question of how to judge the authenticity of predictions. This problem can be converted into the contribution of different face areas to different emotions. In this paper, we divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more convincing results, our experiments are conducted on three different databases: facial expression recognition + ( FER+), real-world affective faces database (RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the classification accuracy, the confusion matrix and the class activation map (CAM), we can establish convincing results. To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis. Our findings can be combined with findings in psychology to promote the understanding of emotional expressions. |
关键词 | Facial emotion analysis face areas class activation map confusion matrix concerned area. |
DOI | 10.1007/s11633-019-1176-9 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42313 |
专题 | 多模态人工智能系统全国重点实验室_智能交互 学术期刊_Machine Intelligence Research |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing 100190, China 2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China 3.CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zheng Lian,Ya Li,Jian-Hua Tao,et al. Expression Analysis Based on Face Regions in Real-world Conditions[J]. International Journal of Automation and Computing,2020,17(1):96-107. |
APA | Zheng Lian,Ya Li,Jian-Hua Tao,Jian Huang,&Ming-Yue Niu.(2020).Expression Analysis Based on Face Regions in Real-world Conditions.International Journal of Automation and Computing,17(1),96-107. |
MLA | Zheng Lian,et al."Expression Analysis Based on Face Regions in Real-world Conditions".International Journal of Automation and Computing 17.1(2020):96-107. |
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IJAC-HCI-2018-09-243(1364KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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