Automatic facial action unit detection from video is a long-standing problem in facial expression analysis. Research has focused on registration, choice of features, and classifiers. A relatively neglected problem is the choice of training images. Nearly all previous work uses one or the other of two standard approaches. One approach assigns peak frames to the positive class and frames associated with other actions to the negative class. This approach maximizes differences between positive and negative classes, but results in a large imbalance between them, especially for infrequent AUs. The other approach reduces imbalance in class membership by including all target frames from onsets to offsets in the positive class. However, because frames near onsets and offsets often differ little from those that precede them, this approach can dramatically increase false positives. We propose a novel alternative, dynamic cascades with bidirectional bootstrapping (DCBB), to select training samples. Using an iterative approach, DCBB optimally selects positive and negative samples in the training data. Using Cascade Adaboost as basic classifier, DCBB exploits the advantages of feature selection, efficiency, and robustness of Cascade Adaboost. To provide a real-world test, we used the RU-FACS (a.k.a. M3) database of nonposed behavior recorded during interviews. For most tested action units, DCBB improved AU detection relative to alternative approaches.

中文翻译：

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具有双向引导的动态级联用于自发面部行为中的动作单元检测


从视频中自动检测面部动作单元是面部表情分析中长期存在的问题。研究重点是注册、特征选择和分类器。一个相对被忽视的问题是训练图像的选择。几乎所有以前的工作都使用两种标准方法中的一种或另一种。一种方法将峰值帧分配给正类，将与其他动作相关的帧分配给负类。这种方法最大化了正类和负类之间的差异，但会导致它们之间的巨大不平衡，特别是对于不频繁的 AU。另一种方法通过将从起始点到偏移量的所有目标框架包含在正类中来减少类成员的不平衡。然而，由于起始点和偏移量附近的帧通常与它们之前的帧几乎没有什么不同，因此这种方法会显着增加误报。我们提出了一种新颖的替代方案，即双向引导的动态级联（DCBB）来选择训练样本。 DCBB 使用迭代方法在训练数据中最优选择正样本和负样本。 DCBB 使用 Cascade Adaboost 作为基本分类器，充分发挥了 Cascade Adaboost 的特征选择、效率和鲁棒性的优势。为了提供真实世界的测试，我们使用了 RU-FACS（又名 M3）数据库，记录了访谈期间记录的非预设行为。对于大多数经过测试的动作单元，DCBB 相对于替代方法改进了 AU 检测。