Towards Convenient Calibration for Cross-Ratio based Gaze Estimation

Eye gaze movements are considered as a salient modality for human computer interaction applications. Recently, cross-ratio (CR) based eye tracking methods have attracted increasing interest because they provide remote gaze estimation using a single uncalibrated camera. However, due to the simplification assumptions in CR-based methods, their performance is lower than the model-based approaches [8]. Several efforts have been made to improve the accuracy by compensating for the assumptions with subject- specific calibration. This paper presents a CR-based automatic gaze estimation system that accurately works under natural head movements. A subject-specific calibration method based on regularized least-squares regression (LSR) is introduced for achieving higher accuracy compared to other state-of-the-art calibration methods. Experimental results also show that the proposed calibration method generalizes better when fewer calibration points are used. This enables user friendly applications with minimum calibration effort without sacrificing too much accuracy. In addition, we adaptively fuse the estimation of the point of regard (PoR) from both eyes based on the visibility of eye features. The adaptive fusion scheme reduces accuracy error by around 20% and also increases the estimation coverage under natural head movements

Estimating Fusion Weights of a Multi-Camera Eye Tracking System by Leveraging User Calibration Data

Cross-ratio (CR)-based eye tracking has been attracting much interest due to its simple setup, yet its accuracy is lower than that of the model-based approaches. In order to improve the estimation accuracy, a multi-camera setup can be exploited rather than the traditional single camera systems. The overall gaze point can be computed by fusion of available gaze information from all cameras. This paper presents a real-time multi-camera eye tracking system in which the estimation of gaze relies on simple CR geometry. A novel weighted fusion method is proposed, which leverages the user calibration data to learn the fusion weights. Experimental results conducted on real data show that the proposed method achieves a significant accuracy improvement over single camera systems. The real-time system achieves 0.82 degrees of visual angle accuracy error with very few calibration data (5 points) under natural head movements, which is competitive with more complex model-based systems

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

A Regression-based User Calibration Framework for Real-time Gaze Estimation

Eye movements play a very significant role in human computer interaction (HCI) as they are natural and fast, and contain important cues for human cognitive state and visual attention. Over the last two decades, many techniques have been proposed to accurately estimate the gaze. Among these, video-based remote eye trackers have attracted much interest since they enable non-intrusive gaze estimation. To achieve high estimation accuracies for remote systems, user calibration is inevitable in order to compensate for the estimation bias caused by person-specific eye parameters. Although several explicit and implicit user calibration methods have been proposed to ease the calibration burden, the procedure is still cumbersome and needs further improvement. In this paper, we present a comprehensive analysis of regression-based user calibration techniques. We propose a novel weighted least squares regression-based user calibration method together with a real-time cross-ratio based gaze estimation framework. The proposed system enables to obtain high estimation accuracy with minimum user effort which leads to user-friendly HCI applications. Experimental results conducted on both simulations and user experiments show that our framework achieves a significant performance improvement over the state-of-the-art user calibration methods when only a few points are available for the calibration

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