Measuring task performance using gaze regions

Abstract

We present a novel method for measuring task performance using gaze regions, i.e., scene regions fixated by a subject as he or she performs a familiar manual task. The scene regions are learned as a bag of features representation, using library lookup based on the Histogram of Oriented Gradients feature descriptor [1]. By establishing a set of task-specific exemplar models, i.e., models sourced from Pareto optimal sequences, the approach recognizes the local optima within a set of task-specific unlabeled models by estimating the distance (of each unlabeled model) to the exemplar models. During testing, the method is evaluated against a dataset of egocentric sequences, each containing gaze data, belonging to three manual skill-based activities. The results show perfect classification's accuracy on several proposed schemes.