Machine learning for recognition of individuals from motion capture time series: performance and explainability

In this paper we describe an ongoing research project in which we investigate the capability of AI-systems to recognize individuals from motion capture data, e.g. using a neural network. In our previous work [1] we also showed which motion features more strongly characterize each individual. In addition, we report on the application of some techniques suggested by Explainable AI's literature. In particular we have analyzed the parsimonious linear fingerprinting (PLiF) [2] and a specific learning shapelets method suggested by Tavenard [3].