  Face identification/recognition has significantly advanced over the past
years. However, most of the proposed approaches rely on static RGB frames and
on neutral facial expressions. This has two disadvantages. First, important
facial shape cues are ignored. Second, facial deformations due to expressions
can have an impact on the performance of such a method. In this paper, we
propose a novel framework for dynamic 3D face identification/recognition based
on facial keypoints. Each dynamic sequence of facial expressions is represented
as a spatio-temporal graph, which is constructed using 3D facial landmarks.
Each graph node contains local shape and texture features that are extracted
from its neighborhood. For the classification/identification of faces, a
Spatio-temporal Graph Convolutional Network (ST-GCN) is used. Finally, we
evaluate our approach on a challenging dynamic 3D facial expression dataset.
