Precise Non-Intrusive Real-Time Gaze Tracking System for Embedded Setups

This paper describes a non-intrusive real-time gaze detection system, characterized by a precise determination of a subject's pupil centre. A narrow field-of-view camera (NFV), focused on one of the subject's eyes follows the head movements in order to keep the pupil centred in the image. When a tracking error is observed, feedback provided by a second camera, in this case a wide field-of-view (WFV) camera, allows quick recovery of the tracking process. Illumination is provided by four infrared LED blocks synchronised with the electronic shutter of the eye camera. The characteristic shape of corneal glints produced by these illuminators allows optimizing the image processing algorithms for gaze detection developed for this system. The illumination power used in this system has been limited to well below maximum recommended levels. After an initial calibration procedure, the line of gaze is determined starting from the vector defined by the pupil centre and a valid glint. The glints are validated using the iris outline to avoid glint distortion produced by changes in the curvature on the ocular globe. In order to minimize measurement error in the pupil-glint vector, algorithms are proposed to determine the pupil centre at sub-pixel resolution. Although the paper describes a desk-mounted prototype, the final implementation is to be installed on board of a conventional car as an embedded system to determine the line of gaze of the driver

Robust Gaze Estimation Based on Adaptive Fusion of Multiple Cameras

Gaze movements play a crucial role in human-computer interaction (HCI) applications. Recently, gaze tracking systems with a wide variety of applications have attracted much interest by the industry as well as the scientific community. The state-of-the-art gaze trackers are mostly non-intrusive and report high estimation accuracies. However, they require complex setups such as camera and geometric calibration in addition to subject-specific calibration. In this paper, we introduce a multi-camera gaze estimation system which requires less effort for the users in terms of the system setup and calibration. The system is based on an adaptive fusion of multiple independent camera systems in which the gaze estimation relies on simple cross-ratio (CR) geometry. Experimental results conducted on real data show that the proposed system achieves a significant accuracy improvement, by around 25%, over the traditional CR-based single camera systems through the novel adaptive multi-camera fusion scheme. The real-time system achieves less than 0.9 degrees accuracy error with very few calibration data (5 points) under natural head movements, which is competitive with more complex systems. Hence, the proposed system enables fast and user-friendly gaze tracking with minimum user effort without sacrificing too much accuracy

High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking Methods

This study investigates the influence of the eye-camera location associated with the accuracy and precision of interpolation-based eye-tracking methods. Several factors can negatively influence gaze estimation methods when building a commercial or off-the-shelf eye tracker device, including the eye-camera location in uncalibrated setups. Our experiments show that the eye-camera location combined with the non-coplanarity of the eye plane deforms the eye feature distribution when the eye-camera is far from the eye’s optical axis. This paper proposes geometric transformation methods to reshape the eye feature distribution based on the virtual alignment of the eye-camera in the center of the eye’s optical axis. The data analysis uses eye-tracking data from a simulated environment and an experiment with 83 volunteer participants (55 males and 28 females). We evaluate the improvements achieved with the proposed methods using Gaussian analysis, which defines a range for high-accuracy gaze estimation between −0.5∘ and 0.5∘. Compared to traditional polynomial-based and homography-based gaze estimation methods, the proposed methods increase the number of gaze estimations in the high-accuracy range

Improving head movement tolerance of cross-ratio based eye trackers

When first introduced, the cross-ratio (CR) based remote eye tracking method offered many attractive features for natural human gaze-based interaction, such as simple camera setup, no user calibration, and invariance to head motion. However, due to many simplification assumptions, current CR-based methods are still sensitive to head movements. In this paper, we revisit the CR-based method and introduce two new extensions to improve the robustness of the method to head motion. The first method dynamically compensates for scale changes in the corneal reflection pattern, and the second method estimates true coplanar eye features so that the cross-ratio can be applied. We present real-time implementations of both systems, and compare the performance of these new methods using simulations and user experiments. Our results show a significant improvement in robustness to head motion and, for the user experiments in particular, an average reduction of up to 40 % in gaze estimation error was observedCAPESFAPESPIB

Robust Eye Tracking Based on Adaptive Fusion of Multiple Cameras

Eye and gaze movements play an essential role in identifying individuals' emotional states, cognitive activities, interests, and attention among other behavioral traits. Besides, they are natural, fast, and implicitly reflect the targets of interest, which makes them a highly valuable input modality in human-computer interfaces. Therefore, tracking gaze movements, in other words, eye tracking is of great interest to a large number of disciplines, including human behaviour research, neuroscience, medicine, and human-computer interaction. Tracking gaze movements accurately is a challenging task, especially under unconstrained conditions. Over the last two decades, significant advances have been made in improving the gaze estimation accuracy. However, these improvements have been achieved mostly under controlled settings. Meanwhile, several concerns have arisen, such as the complexity, inflexibility and cost of the setups, increased user effort, and high sensitivity to varying real-world conditions. Despite various attempts and promising enhancements, existing eye tracking systems are still inadequate to overcome most of these concerns, which prevent them from being widely used. In this thesis, we revisit these concerns and introduce a novel multi-camera eye tracking framework. The proposed framework achieves a high estimation accuracy while requiring a minimal user effort and a non-intrusive flexible setup. In addition, it provides improved robustness to large head movements, illumination changes, use of eye wear, and eye type variations across users. We develop a novel real-time gaze estimation framework based on adaptive fusion of multiple single-camera systems, in which the gaze estimation relies on projective geometry. Besides, to ease the user calibration procedure, we investigate several methods to model the subject-specific estimation bias, and consequently, propose a novel approach based on weighted regularized least squares regression. The proposed method provides a better calibration modeling than state-of-the-art methods, particularly when using low-resolution and limited calibration data. Being able to operate with low-resolution data also enables to utilize a large field-of-view setup, so that large head movements are allowed. To address aforementioned robustness concerns, we propose to leverage multiple eye appearances simultaneously acquired from various views. In comparison with conventional single view approach, the main benefit of our approach is to more reliably detect gaze features under challenging conditions, especially when they are obstructed due to large head pose or movements, or eye glasses effects. We further propose an adaptive fusion mechanism to effectively combine the gaze outputs obtained from multi-view appearances. To this effect, our mechanism firstly determines the estimation reliability of each gaze output and then performs a reliability-based weighted fusion to compute the overall point of regard. In addition, to address illumination and eye type robustness, the setup is built upon active illumination and robust feature detection methods are developed. The proposed framework and methods are validated through extensive simulations and user experiments featuring 20 subjects. The results demonstrate that our framework provides not only a significant improvement in gaze estimation accuracy but also a notable robustness to real-world conditions, making it suitable for a large spectrum of applications