A method to compensate head movements for mobile eye tracker using invisible markers

Although mobile eye-trackers have wide measurement range of gaze, and high flexibility, it is difficult to judge what a subject is actually looking at based only on obtained coordinates, due to the influence of head movement. In this paper, a method to compensate for head movements while seeing the large screen with mobile eye-tracker is proposed, through the use of NIR-LED markers embedded on the screen. The head movements are compensated by performing template matching on the images of view camera to detect the actual eye position on the screen. As a result of the experiment, the detection rate of template matching was 98.6%, the average distance between the actual eye position and the corrected eye position was approximately 16 pixels for the projected image (1920 x 1080)

Tracking Gaze and Visual Focus of Attention of People Involved in Social Interaction

The visual focus of attention (VFOA) has been recognized as a prominent conversational cue. We are interested in estimating and tracking the VFOAs associated with multi-party social interactions. We note that in this type of situations the participants either look at each other or at an object of interest; therefore their eyes are not always visible. Consequently both gaze and VFOA estimation cannot be based on eye detection and tracking. We propose a method that exploits the correlation between eye gaze and head movements. Both VFOA and gaze are modeled as latent variables in a Bayesian switching state-space model. The proposed formulation leads to a tractable learning procedure and to an efficient algorithm that simultaneously tracks gaze and visual focus. The method is tested and benchmarked using two publicly available datasets that contain typical multi-party human-robot and human-human interactions.Comment: 15 pages, 8 figures, 6 table

EyeSee3D: a low-cost approach for analysing mobile 3D eye tracking data using augmented reality technology

Pfeiffer T, Renner P. EyeSee3D: a low-cost approach for analysing mobile 3D eye tracking data using augmented reality technology. In: Proceedings of the Symposium on Eye Tracking Research and Applications. New York: ACM; 2014: 195-202.For validly analyzing human visual attention, it is often necessary to proceed from computer-based desktop set-ups to more natural real-world settings. However, the resulting loss of control has to be counterbalanced by increasing participant and/or item count. Together with the effort required to manually annotate the gaze-cursor videos recorded with mobile eye trackers, this renders many studies unfeasible. We tackle this issue by minimizing the need for manual annotation of mobile gaze data. Our approach combines geo\-metric modelling with inexpensive 3D marker tracking to align virtual proxies with the real-world objects. This allows us to classify fixations on objects of interest automatically while supporting a completely free moving participant. The paper presents the EyeSee3D method as well as a comparison of an expensive outside-in (external cameras) and a low-cost inside-out (scene camera) tracking of the eyetracker's position. The EyeSee3D approach is evaluated comparing the results from automatic and manual classification of fixation targets, which raises old problems of annotation validity in a modern context

Eye center localization and gaze gesture recognition for human-computer interaction

© 2016 Optical Society of America. This paper introduces an unsupervised modular approach for accurate and real-time eye center localization in images and videos, thus allowing a coarse-to-fine, global-to-regional scheme. The trajectories of eye centers in consecutive frames, i.e., gaze gestures, are further analyzed, recognized, and employed to boost the human-computer interaction (HCI) experience. This modular approach makes use of isophote and gradient features to estimate the eye center locations. A selective oriented gradient filter has been specifically designed to remove strong gradients from eyebrows, eye corners, and shadows, which sabotage most eye center localization methods. A real-world implementation utilizing these algorithms has been designed in the form of an interactive advertising billboard to demonstrate the effectiveness of our method for HCI. The eye center localization algorithm has been compared with 10 other algorithms on the BioID database and six other algorithms on the GI4E database. It outperforms all the other algorithms in comparison in terms of localization accuracy. Further tests on the extended Yale Face Database b and self-collected data have proved this algorithm to be robust against moderate head poses and poor illumination conditions. The interactive advertising billboard has manifested outstanding usability and effectiveness in our tests and shows great potential for benefiting a wide range of real-world HCI applications

Proceedings of the 1st joint workshop on Smart Connected and Wearable Things 2016

These are the Proceedings of the 1st joint workshop on Smart Connected and Wearable Things (SCWT'2016, Co-located with IUI 2016). The SCWT workshop integrates the SmartObjects and IoWT workshops. It focusses on the advanced interactions with smart objects in the context of the Internet-of-Things (IoT), and on the increasing popularity of wearables as advanced means to facilitate such interactions