Raymond W. Ptucha

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This paper exploits the discriminative power of manifold learning in conjunction with the parsimonious power of sparse signal representation to perform robust facial expression recognition. By utilizing an &#x2113;<sup>1</sup> reconstruction error and a statistical mixture model, both accuracy and tolerance to occlusion improve without the need to perform(More)
The parsimonious nature of sparse representations has been successfully exploited for the development of highly accurate classifiers for various scientific applications. Despite the successes of Sparse Representation techniques, a large number of dictionary atoms as well as the high dimensionality of the data can make these classifiers computationally(More)
This paper introduces an interactive display system guided by a human observer's gesture, facial pose, and facial expression. The Kinect depth sensor is used to detect and track an observer's skeletal joints while the RGB camera is used for detailed facial analysis. The display consists of active regions that the observer can manipulate with body gestures(More)
Sparse representations have successfully been exploited for the development of highly accurate classifiers. Unfortunately, these classifiers are computationally intensive and subject to the adverse effects of coefficient contamination, where for example variations in pose may affect identity and expression recognition. We propose a technique, called(More)
Domestic abuse affects people of every race, class, age, and nation. There is significant research on the prevalence and effects of domestic abuse; however, such research typically involves population-based surveys that have high financial costs. This work provides a qualitative analysis of domestic abuse using data collected from the social and(More)