University of Houston

Accurate tracking of facial tissue in thermal infrared imaging is challenging because it is affected not only by positional but also physiological (functional) changes. This dissertation presents a particle filter tracker driven by a probabilistic template function with both spatial and temporal smoothing components, which is capable of adapting to abrupt positional and physiological changes. The method was tested on tracking facial regions of subjects under varying physiological and environmental conditions in 12 thermal clips. It demonstrated robustness and accuracy, outperforming other strategies. This new method promises improved performance in a host of applications from biometrics (contact-free polygraphy) to biomedicine (unobtrusive sleep studies).