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Parametric temporal alignment for the detection of facial action temporal segments

机译:用于检测面部动作时间片段的参数时间对齐

摘要

In this paper we propose the very first weakly supervised approach for detecting facial action unit temporal segments. This is achieved by means of behaviour similarity matching, where no training of dedicated classifiers is needed and the input facial behaviour episode is compared to a template. The inferred temporal segment boundaries of the test sequence are those transferred from the template sequence. To this end, a parametric temporal alignment algorithm is proposed to align a single exemplar sequence to the test sequence. The proposed strategy can accommodate flexible time warp functions, does not need to exhaustively align all frames in both sequences, and the optimal warp parameters can be found by an efficient Gauss-Newton gradient descent search. We show that our approach produces the best results to date for the problem at hand, and provides a promising opportunity to studying facial actions from a new perspective.
机译:在本文中,我们提出了第一个用于检测面部动作单元时间段的弱监督方法。这是通过行为相似性匹配来实现的,其中不需要训练专用的分类器,并且将输入的面部行为情节与模板进行比较。测试序列的推断时间片段边界是从模板序列转移的那些。为此,提出了一种参数化的时间比对算法,以将单个示例序列与测试序列进行比对。所提出的策略可以适应灵活的时间扭曲函数,不需要在两个序列中穷举对齐所有帧,并且可以通过有效的高斯-牛顿梯度下降搜索找到最优的扭曲参数。我们证明了我们的方法迄今为止可以解决当前的问题,并且为从新的角度研究面部动作提供了一个有希望的机会。

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