Abstract
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.
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Faithfull, W.J., Kuncheva, L.I. (2014). On Optimum Thresholding of Multivariate Change Detectors. In: Fränti, P., Brown, G., Loog, M., Escolano, F., Pelillo, M. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2014. Lecture Notes in Computer Science, vol 8621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44415-3_37
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