Abstract
Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.
Highlights
It has long been recognized that human interaction encompasses multiple channels [1]
We provide a description of the category, the input type, computational details, lighting conditions, details about user-dependent calibration, error, and discussion of application in a real human-computer interaction (HCI)/human-robot interaction (HRI) scenario
We evaluated the performance of the the gaze estimation algorithm by means of leave-one-out cross-validation (Sec. 4.3.2)
Summary
It has long been recognized that human interaction encompasses multiple channels [1]. Accurate eye gaze tracking normally requires expensive specialized hardware (such as the eye-tracking solutions produced by Tobii [3] or SR Research [4]) that relies on active sensing (most commonly, infrared illuminators)[5]. This reduces the appeal of these systems for consumer market applications [6]. These solutions often require a manual calibration procedure for each new user
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