In order to analyze expressions that are different from the prototypic expressions defined by Ekman, manifold learning has been proposed to build person-specific continuous representations of facial expressions. Yet, it is still a challenging problem to build such a manifold with no prior knowledge on the morphology of the subject. Here, we propose a method to build a person-specific manifold of facial expressions able to adapt to the morphology of the subject in an unsupervised manner. The manifold is initialized with the facial landmarks of the neutral face and 5 synthesized basic expressions. Our first contribution is to detect automatically the neutral face of the subject so that we can build the manifold in an unsupervised manner. Our second and main contribution is to adapt in an unsupervised manner the initialized manifold to the morphology of the subject by detecting the real basic expressions of the subject while maintaining constraints in the manifold. Our third contribution is to perform the adaptation on spontaneous expressions with typical head pose variation for human-computer interaction. The experiments show that the adaptation works well on posed expressions and that the constraints for the adaptation on spontaneous expressions is efficient when head pose variation is considered.

中文翻译：

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特定人面部表情流形的无监督适应

为了分析不同于 Ekman 定义的原型表情的表情，已经提出了流形学习来构建特定于人的面部表情的连续表示。然而，在没有对象形态学的先验知识的情况下构建这样的流形仍然是一个具有挑战性的问题。在这里，我们提出了一种方法来构建特定于人的面部表情流形，能够以无监督的方式适应主体的形态。流形用中性人脸的面部标志和 5 个合成的基本表情进行初始化。我们的第一个贡献是自动检测主体的中性面孔，以便我们可以以无监督的方式构建流形。我们的第二个也是主要的贡献是通过在保持流形中的约束的同时检测主体的真实基本表达式，以无监督的方式使初始化的流形适应主体的形态。我们的第三个贡献是对具有典型头部姿势变化的自发表情进行适应，以进行人机交互。实验表明，自适应在姿势表情上效果很好，并且当考虑头部姿势变化时，对自发表情的自适应约束是有效的。