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4.针对微表情的特点,使用Robust PCA从微表情视频中进一步的抽取细微的微表情运动信息,去除去身份信息。身份信息在微表情视频中占有很大的比重,相对与微表情识别任务来说,身份信息属于噪声。

Other Abstract

is a fast leaked facial expression which is characterized by its short duration and low intensity. It can be effectively applied in lie detection as well as many other fields of studies. The research employs computer vision techniques and the research methods from cognitive psychology to develop micro-expression automatic recognition algorithms and models. Constructing two databases for micro-expression recognition. Analyzing the color space and utilize color information to further increase the accuracy of micro-expression recognition. To address the characteristic of micro- expression, we investigate the sparse representation of micro- expressions and represent micro-expressions as tensors to preserve its temporal information. Solutions presented in this report can be summarized as follows:

1.We use the psychological methods to elicit micro-expressions and use high-speed camera to capture them. Then two micro-expression databases are built and released.

2.We extend DTSA to high-order tensor and use Extreme Learning Machine to classify micro-expression.

3.We analyze color space and propose a novel color space, tensor independent color space (TICS). In TICS, micro-expression recognition gets better performance.

4.For the characteristics of micro-expressions, We use Robust PCA to extract subtle motion information from micro一expression video clips.

Keyword微表情识别 颜色空间 张量分析 稀疏表示
Document Type科技报告
Recommended Citation
GB/T 7714
王甦菁. 基于稀疏张量的彩色微表情识别[R]. 北京,2015.
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