Recognizing expressions from face and body gesture by temporal normalized motion and appearance features
Recognizing expressions from face and body gesture by temporal normalized motion and appearance features
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DOI：
10.1109/cvprw.2011.5981880
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Shizhi Chen;Yingli Tian;Qingshan Liu;Dimitris N. Metaxas
中科院分区：
文献类型：
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作者：
Shizhi Chen;Yingli Tian;Qingshan Liu;Dimitris N. Metaxas
最近，识别面部和身体手势的影响引起了更多的关注。然而，它仍然缺乏高效和有效的功能来描述动态的人脸和手势的实时自动识别的影响。在本文中，我们提出了一种新的方法，它结合了MHI-HOG和Image-HOG通过时间归一化的方法，来描述动态的人脸和身体姿态的情感识别。MHI-HOG代表运动历史图像（MHI）上的方向分量直方图（HOG）。它捕捉运动方向的兴趣点作为一个表达式的演变随着时间的推移。Image-HOG捕获相应兴趣点的外观信息。结合MHI-HOG和Image-HOG可以有效地表示局部运动和外观信息的人脸和身体姿态的情感识别。时间归一化方法显式地解决了基于视频的情感识别中的时间分辨率问题。实验结果表明，有前途的性能相比，最先进的。我们还表明，表达识别与时间动态优于基于帧的识别。
Recently, recognizing affects from both face and body gestures attracts more attentions. However, it still lacks of efficient and effective features to describe the dynamics of face and gestures for real-time automatic affect recognition. In this paper, we propose a novel approach, which combines both MHI-HOG and Image-HOG through temporal normalization method, to describe the dynamics of face and body gestures for affect recognition. The MHI-HOG stands for Histogram of Oriented Gradients (HOG) on the Motion History Image (MHI). It captures motion direction of an interest point as an expression evolves over the time. The Image-HOG captures the appearance information of the corresponding interesting point. Combination of MHI-HOG and Image-HOG can effectively represent both local motion and appearance information of face and body gesture for affect recognition. The temporal normalization method explicitly solves the time resolution issue in the video-based affect recognition. Experimental results demonstrate promising performance as compared with the state of the art. We also show that expression recognition with temporal dynamics outperforms frame-based recognition.