An auxiliary gaze point estimation method based on facial normal

Abstract

Considering the main disadvantage of the existing gaze point estimation methods which restrict user’s head movement and have potential injury on eyes, we propose a gaze point estimation method based on facial normal and binocular vision. Firstly, we calibrate stereo cameras to determine the extrinsic and intrinsic parameters of the cameras; Secondly, face is quickly detected by Viola–Jones framework and the center position of the two irises can be located based on integro-differential operators; The two nostrils and mouth are detected based on the saturation difference and their 2D coordinates can be calculated; Thirdly, the 3D coordinates of these five points are obtained by stereo matching and 3D reconstruction; After that, a plane fitting algorithm based on least squares is adopted to get the approximate facial plane, then, the normal via the midpoint of the two pupils can be figured out; Finally, the point-of-gaze can be obtained by getting the intersection point of the facial normal and the computer screen. Experimental results confirm the accuracy and robustness of the proposed method.

Predicting the Sixteen Personality Factors (16PF) of an individual by analyzing facial features

“…A rather new approach is the use of slow feature analysis (SFA) for dynamic time-varying scenarios [47] with the main advantage of being able to find uncorrelated projections by means of an Expectation-Maximization (EM) algorithm. Neural networks have also been used in FER systems, specifically Long Short-Term-Memory Recurrent Neural Networks (LSTM-RMM) [15]. The proposed method defines a set of Continuous Conditional Random Fields (CCRF) that are used to predict emotions from both encephalogram (EEG) signals and facial features.…”

“…Although at first focused on predicting the emotional state of people [12,13], as Facial Expression Recognition (FER) systems gained momentum and started achieving acceptable prediction accuracy, recent research papers have begun using facial features analysis for more complex tasks, such as tracking and predicting eye gaze [14,15], predicting driver attention for car accident prevention [14,16], predicting stress levels [2,17], diagnosing depression [3], assessing the facial attractiveness of individuals [18], evaluating people's trust [19], and predicting personality traits [4,[20][21][22][23]. All these research studies showed that the face indeed conveys information that can be analyzed to predict different psychological features of an individual.…”

A Bibliometric Study on Eye-Tracking Research in Tourism

“…In eye movement analysis, some metric data based on fi xation and saccade measurements are gathered. "Gaze points" derived from fi xations and saccades are the collection of data that describe where the eye is gazing (Sun et al, 2016). A metric similar to gaze points is "heat maps," which present an intensity map of where and at what intensity the eyes look (Pfeiff er & Memili, 2016).…”