Facial spatial patterns can help distinguish between posed and spontaneous expressions, but this information has not been thoroughly leveraged by current studies. We present several latent regression Bayesian networks (LRBNs) to capture the patterns existing in facial landmark points and to use those points to differentiate posed from spontaneous expressions. The visible nodes of the LRBN represent facial landmark points. Through learning, the LRBN captures the probabilistic dependencies among landmark points as well as latent variables given observations, successfully modeling the spatial patterns inherent in expressions. Current methods tend to ignore gender and expression categories, although these factors can influence spatial patterns. Therefore, we propose to incorporate this as a kind of privileged information. We construct several LRBNs to capture spatial patterns from spontaneous and posed facial expressions given expression-related factors. Facial landmark points are used during testing to classify samples as either posed or spontaneous, depending on which LRBN has the largest likelihood. We conduct experiments to showcase the superiority of the proposed approach in both modeling spatial patterns and classifying expressions as either posed or spontaneous.

中文翻译：

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使用潜在回归贝叶斯网络区分姿势和自发表达

面部空间模式可以帮助区分姿势表情和自发表情，但目前的研究尚未充分利用这些信息。我们提出了几个潜在回归贝叶斯网络 (LRBN) 来捕捉面部标志点中存在的模式，并使用这些点来区分姿势和自发表情。LRBN 的可见节点代表面部标志点。通过学习，LRBN 捕捉到地标点之间的概率依赖关系以及给定观察的潜在变量，成功地对表达式中固有的空间模式进行建模。当前的方法倾向于忽略性别和表达类别，尽管这些因素会影响空间模式。因此，我们建议将其合并为一种特权信息。我们构建了几个 LRBN 来从给定表情相关因素的自发和姿势面部表情中捕捉空间模式。在测试期间使用面部标志点将样本分类为姿势或自发，这取决于哪个 LRBN 具有最大的可能性。我们进行实验以展示所提出的方法在建模空间模式和将表情分类为姿势或自发方面的优越性。