Unsupervised Adaptation of a Person-Specific Manifold of Facial Expressions
Raphaël Weber, Vincent Barrielle, Catherine Soladié, Renaud Séguier · IEEE Transactions on Affective Computing · 2018
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.