On Optimum Thresholding Of Multivariate Change Detectors

A change detection algorithm for multi-dimensional data reduces the input space to a single statistic and compares it with a threshold to signal change. This study investigates the performance of two methods for estimating such a threshold: bootstrapping and control charts. The methods are tested on a challenging dataset of emotional facial expressions, recorded in real-time using Kinect for Windows. Our results favoured the control chart threshold and suggested a possible benefit from using multiple detectors.