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摘要: 针对低维隐变量分布的连通性问题提出了表情动作单元(Facial action units, FAU)跟踪的隧道隐变量法. 该方法通过有侧重的随机跳转克服了隐变量连通性不足所导致的局部收敛. 实验表明该方法较普通隐变量法具有较好的鲁棒性和FAU跟踪精度.Abstract: A nonlinear data reduction process with limited training examples usually results in a latent variables space with some unpleasant disconnections which may cause a tracking procedure to fail at local minimums. This paper presents a novel method to resolve the problem. It is used to track facial action units in the video and the experiment results are encouraging.
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Key words:
- Nonlinear data reduction /
- Gaussian process (GP) /
- cluster analysis /
- particle filtering
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