An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices

In recent years we have witnessed an increasing number of interactive systems on handheld mobile devices which utilise gaze as a single or complementary interaction modality. This trend is driven by the enhanced computational power of these devices, higher resolution and capacity of their cameras, and improved gaze estimation accuracy obtained from advanced machine learning techniques, especially in deep learning. As the literature is fast progressing, there is a pressing need to review the state of the art, delineate the boundary, and identify the key research challenges and opportunities in gaze estimation and interaction. This paper aims to serve this purpose by presenting an end-to-end holistic view in this area, from gaze capturing sensors, to gaze estimation workflows, to deep learning techniques, and to gaze interactive applications.PostprintPeer reviewe

Customer Gaze Estimation in Retail Using Deep Learning

At present, intelligent computing applications are widely used in different domains, including retail stores. The analysis of customer behaviour has become crucial for the benefit of both customers and retailers. In this regard, the concept of remote gaze estimation using deep learning has shown promising results in analyzing customer behaviour in retail due to its scalability, robustness, low cost, and uninterrupted nature. This study presents a three-stage, three-attention-based deep convolutional neural network for remote gaze estimation in retail using image data. In the first stage, we design a mechanism to estimate the 3D gaze of the subject using image data and monocular depth estimation. The second stage presents a novel three-attention mechanism to estimate the gaze in the wild from field-of-view, depth range, and object channel attentions. The third stage generates the gaze saliency heatmap from the output attention map of the second stage. We train and evaluate the proposed model using benchmark GOO-Real dataset and compare results with baseline models. Further, we adapt our model to real-retail environments by introducing a novel Retail Gaze dataset. Extensive experiments demonstrate that our approach significantly improves remote gaze target estimation performance on GOO-Real and Retail Gaze datasets

Multi-User Eye-Tracking

The human gaze characteristics provide informative cues on human behavior during various activities. Using traditional eye trackers, assessing gaze characteristics in the wild requires a dedicated device per participant and therefore is not feasible for large-scale experiments. In this study, we propose a commodity hardware-based multi-user eye-tracking system. We leverage the recent advancements in Deep Neural Networks and large-scale datasets for implementing our system. Our preliminary studies provide promising results for multi-user eye-tracking on commodity hardware, providing a cost-effective solution for large-scale studies

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

I2DNet - Design and real-time evaluation of an appearance-based gaze estimation system

Gaze estimation problem can be addressed using either model-based or appearance-based approaches. Model-based approaches rely on features extracted from eye images to fit a 3D eye-ball model to obtain gaze point estimate while appearance-based methods attempt to directly map captured eye images to gaze point without any handcrafted features. Recently, availability of large datasets and novel deep learning techniques made appearance-based methods achieve superior accuracy than model-based approaches. However, many appearance-based gaze estimation systems perform well in within-dataset validation but fail to provide the same degree of accuracy in cross-dataset evaluation. Hence, it is still unclear how well the current state-of-the-art approaches perform in real-time in an interactive setting on unseen users. This paper proposes I2DNet, a novel architecture aimed to improve subject-independent gaze estimation accuracy that achieved a state-of-the-art 4.3 and 8.4 degree mean angle error on the MPIIGaze and RT-Gene datasets respectively. We have evaluated the proposed system as a gaze-controlled interface in real-time for a 9-block pointing and selection task and compared it with Webgazer.js and OpenFace 2.0. We have conducted a user study with 16 participants, and our proposed system reduces selection time and the number of missed selections statistically significantly compared to other two systems