Priori-driven dimensions of face-space: Experiments incorporating eye-tracking information

Face-space has become established as an effective model for representing the dimensions of variation that occur in collections of human faces. For example, a change of expression from neutral to smiling can be represented by one axis in a face space. Principal components can be used to determine the axes of a face-space, however, standard principal components are based entirely on the data set from which they are computed, and do not express any domain specific information about the application of interest. In this paper, we propose a face-space analysis that combines the variance criterion used in principal components with some prior knowledge about the task-driven experiment. The priors are based on measuring eye movements of participants to frontal 2D faces during separate gender and facial expression categorization tasks. Our findings show that saccades to faces are task-driven, especially from 500 to 1000 milliseconds, and automatic recognition performance does not improve with additional exposure time.