Eye movements and human face perception: An holistic analysis and proficiency classification based on frontal 2D face images

Abstract

Human faces convey a collection of information, such as gender, identity, and emotional states. Therefore, understanding the differences between volunteers’ eye movements on benchmark tests of face recognition and perception can explicitly indicate the most discriminating regions to improve performance in this visual cognitive task. The aim of this work is to qualify and classify these eye strategies using multivariate statistics and machine learning techniques, achieving up to 94.8% accuracy. Our experimental results show that volunteers have focused their visual attention, on average, at the eyes, but those with superior performance in the tests carried out have looked at the nose region more closely.