Soft Biometrics by Modeling Temporal Series of Gaze Cues Extracted in the Wild

Soft biometric systems have spread among recent years, both for powering classical biometrics, as well as stand alone solutions with several application scopes ranging from digital signage to human-robot interaction. Among all, in the recent years emerged the possibility to consider as a soft biometrics also the temporal evolution of the human gaze and some recent works in the literature explored this exciting research line by using expensive and (perhaps) unsafe devices which, moreover, require user cooperation to be calibrated. By our knowledge the use of a low-cost, non-invasive, safe and calibration-free gaze estimator to get soft-biometrics data has not been investigated yet. This paper fills this gap by analyzing the soft-biometrics performances obtained by modeling the series of gaze estimated by exploiting the combination of head poses and eyes' pupil locations on data acquired by an off-the-shelf RGB-D device.