Personalization of Gaze Direction Estimation with Deep Learning

Abstract

There is a growing interest in behavior based biometrics. Although biometric data has considerable variations for an individual and may be faked, yet the combination of such 'weak experts' can be rather strong. A remotely detectable component is gaze direction estimation and thus, eye movement patterns. Here, we present a novel personalization method for gaze estimation systems, which does not require a precise calibration setup, can be non-obtrusive, is fast and easy to use. We show that it improves the precision of gaze direction estimation algorithms considerably. The method is convenient; we exploit 3D face model reconstruction for the enrichment of a small number of collected data artificially.