Unsupervised clustering of people from ‘skeleton’ data

This paper investigates the possibility of recognising individual persons from their walking gait using three-dimensional `skeleton' data from an inexpensive consumer-level sensor, the Microsoft `Kinect'. In an experimental pilot study it is shown that the K-means algorithm - as a candidate unsupervised clustering algorithm - is able to cluster gait samples from four persons with a nett accuracy of 43.6%.