Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

A Differential Approach for Gaze Estimation

Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.Comment: Extension to our paper A differential approach for gaze estimation with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine Intelligenc

Leveraging Eye Structure and Motion to Build a Low-Power Wearable Gaze Tracking System

Clinical studies have shown that features of a person\u27s eyes can function as an effective proxy for cognitive state and neurological function. Technological advances in recent decades have allowed us to deepen this understanding and discover that the actions of the eyes are in fact very tightly coupled to the operation of the brain. Researchers have used camera-based eye monitoring technology to exploit this connection and analyze mental state across across many different metrics of interest. These range from simple things like attention and scene processing, to impairments such as a fatigue or substance use, and even significant mental disorders such as Parkinson\u27s, autism, and schizophrenia. While there is a wealth of knowledge and social benefit to be gained from eye tracking, the field has historically been restricted to laboratory use by crippling technological limitations - most notably, device size and power consumption. These issues primarily stem from the use of high-resolution cameras and heavyweight video-processing algorithms, both of which induce extremely high performance overhead on the eye tracker. To address this problem, we have constructed a lightweight, ultra-low-power eye monitoring device in the form factor of a pair of eyeglasses. The key guiding design principle for its construction was saliency-aware resource minimization. Specifically, our design leverages the fact that close-up images of the eye are characterized by large salient features which provide a high degree of redundant information; we exploit this to heavily subsample the eye image and reduce resource utilization while performing effective eye tracking. In the first part of this thesis, we present an initial design of a wearable system to enable ubiquitous eye tracking. By exploiting the fact that the eye has several large, visually redundant features such as the iris and pupil, we were able to develop a neural-network-based adaptive-sampling algorithm for predicting the gaze point while sampling a minimal number of pixels from the image. This enabled us to realize a power savings using specialized imaging hardware that would sample only those most salient pixels, which proportionally reduced the power and time cost of reading images for eye tracking. With these optimizations we were able to build a first-of-of its kind wearable eye tracker that consumed 40 mW of power and demonstrated a gaze tracking error of only 3 degrees across multiple subjects. We refer to this device as the iShadow platform. The second contribution and section of this thesis is a significant improvement upon the original iShadow design for the purpose of improving both power utilization and eye tracking performance. We constructed a new pupil-tracking algorithm based on lightweight computer vision features, which leverages the smoothness of the eye\u27s motion to reduce even further the amount of camera sampling needed. To guard against very infrequent discontinuities resulting from blinks or reflections off the eye, we integrated this model with the previously-used one-shot neural network algorithm. Because the common case (smooth, uninterrupted eye motion) occurs 90% of the time, we were able to realize a dramatic increase in performance due to the efficiency of the smooth tracking algorithm. The new and improved system, labeled CIDER, enabled much more accurate eye tracking - 0.4 degree error - with power consumption as low as 7 mW. This design also enabled a tradeoff between power consumption and eye tracking rate, in which it was also possible to draw higher power of ~30 mW in order to do eye tracking at rates of up to 240 frames per second. The final contribution of this thesis is a re-designed version of the iShadow glasses hardware that is suitable for ``in-the-wild\u27\u27 studies on subjects in their daily living environment. A wearable device, especially one that is worn on the head, must be minimally obtrusive in order to be accepted and used in the field by subjects. This design goal conflicts with the ideal placement of cameras that is needed for achieving consistent eye tracking fidelity. We present multiple possible methods we explored for addressing these competing design challenges, and discuss the reasons that many proved infeasible. To conclude, we present a working design solution that appears to optimally trade off user comfort and convenience and against the technical requirements of the system

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

Estimation of a focused object using a corneal surface image for eye-based interaction

Researchers are considering the use of eye tracking in head-mounted camera systems, such as Google’s Project Glass. Typical methods require detailed calibration in advance, but long periods of use disrupt the calibration record between the eye and the scene camera. In addition, the focused object might not be estimated even if the point-of-regard is estimated using a portable eye-tracker. Therefore, we propose a novel method for estimating the object that a user is focused upon, where an eye camera captures the reflection on the corneal surface. Eye and environment information can be extracted from the corneal surface image simultaneously. We use inverse ray tracing to rectify the reflected image and a scale-invariant feature transform to estimate the object where the point-of-regard is located. Unwarped images can also be generated continuously from corneal surface images. We consider that our proposed method could be applied to a guidance system and we confirmed the feasibility of this application in experiments that estimated the object focused upon and the point-of-regard