.png){kind=icon}
返回
Facial expression recognition on multiple manifolds
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2010.07.017.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Manifold learning has been successfully applied to facial expression recognition by modeling different expressions as a smooth manifold embedded in a high dimensional space. However, the assumption of single manifold is still arguable and therefore does not necessarily guarantee the best classification accuracy. In this paper, a generalized framework for modeling and recognizing facial expressions on multiple manifolds is presented which assumes that different expressions may reside on different manifolds of possibly different dimensionalities. The intrinsic features of each expression are firstly learned separately and the genetic algorithm (GA) is then employed to obtain the nearly optimal dimensionality of each expression manifold from the classification viewpoint. Classification is performed under a newly defined criterion that is based on the minimum reconstruction error on manifolds. Extensive experiments on both the Cohn-Kanade and Feedtum databases show the effectiveness of the proposed multiple manifold based approach. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Facial expression recognition
Classification
Multiple manifolds
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Ability of troponin I to predict cardiac events in patients admitted from the emergency department
.png){kind=icon}
PREAI
分享收藏
Effects of stereoisomers of estradiol on food intake, body weight and hoarding behavior in female rats雌二醇立体异构体对雌性大鼠摄食、体重和囤积行为的影响
PREAI
分享收藏
查看更多内容