Happy Emotion Recognition From Unconstrained Videos Using 3D Hybrid Deep Features
文献类型：期刊论文
| 作者 | Samadiani, Najmeh1; Huang, Guangyan1; Hu, Yu2; Li, Xiaowei2 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2021 |
| 卷号 | 9页码:35524-35538 |
| 关键词 | Feature extraction Emotion recognition Face recognition Videos Three-dimensional displays Long short term memory Visualization Facial landmarks facial expression recognition long short term memory multi-layer neural networks happy emotion recognition |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2021.3061744 |
| 英文摘要 | Facial expressions have been proven to be the most effective way for the brain to recognize human emotions in a variety of contexts. With the exponentially increasing research for emotion detection in recent years, facial expression recognition has become an attractive, hot research topic to identify various basic emotions. Happy emotion is one of such basic emotions with many applications, which is more likely recognized by facial expressions than other emotion measurement instruments (e.g., audio/speech, textual and physiological sensing). Nowadays, most methods have been developed for identifying multiple types of emotions, which aim to achieve the best overall precision for all emotions; it is hard for them to optimize the recognition accuracy for single emotion (e.g., happiness). Only a few methods are designed to recognize single happy emotion captured in the unconstrained videos; however, their limitations lie in that the processing of severe head pose variations has not been considered, and the accuracy is still not satisfied. In this paper, we propose a Happy Emotion Recognition model using the 3D hybrid deep and distance features (HappyER-DDF) method to improve the accuracy by utilizing and extracting two different types of deep visual features. First, we employ a hybrid 3D Inception-ResNet neural network and long-short term memory (LSTM) to extract dynamic spatial-temporal features among sequential frames. Second, we detect facial landmarks' features and calculate the distance between each facial landmark and a reference point on the face (e.g., nose peak) to capture their changes when a person starts to smile (or laugh). We implement the experiments using both feature-level and decision-level fusion techniques on three unconstrained video datasets. The results demonstrate that our HappyER-DDF method is arguably more accurate than several currently available facial expression models. |
| 资助项目 | Australia Research Council (ARC)[DP190100587] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000637163800001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16777]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Huang, Guangyan |
| 作者单位 | 1.Deakin Univ, Sch Informat Technol, Burwood, Vic 3125, Australia 2.Univ Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Samadiani, Najmeh,Huang, Guangyan,Hu, Yu,et al. Happy Emotion Recognition From Unconstrained Videos Using 3D Hybrid Deep Features[J]. IEEE ACCESS,2021,9:35524-35538. |
| APA | Samadiani, Najmeh,Huang, Guangyan,Hu, Yu,&Li, Xiaowei.(2021).Happy Emotion Recognition From Unconstrained Videos Using 3D Hybrid Deep Features.IEEE ACCESS,9,35524-35538. |
| MLA | Samadiani, Najmeh,et al."Happy Emotion Recognition From Unconstrained Videos Using 3D Hybrid Deep Features".IEEE ACCESS 9(2021):35524-35538. |
入库方式： OAI收割
来源：计算技术研究所
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