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 Coarse-to-Fine Adaptive Network for Appearance-Based Gaze Estimation

Human gaze is essential for various appealing applications. Aiming at more accurate gaze estimation, a series of recent works propose to utilize face and eye images simultaneously. Nevertheless, face and eye images only serve as independent or parallel feature sources in those works, the intrinsic correlation between their features is overlooked. In this paper we make the following contributions: 1) We propose a coarse-to-fine strategy which estimates a basic gaze direction from face image and refines it with corresponding residual predicted from eye images. 2) Guided by the proposed strategy, we design a framework which introduces a bi-gram model to bridge gaze residual and basic gaze direction, and an attention component to adaptively acquire suitable fine-grained feature. 3) Integrating the above innovations, we construct a coarse-to-fine adaptive network named CA-Net and achieve state-of-the-art performances on MPIIGaze and EyeDiap.Comment: 9 pages, 7figures, AAAI-2

PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation

Although recent deep learning based gaze estimation approaches have achieved much improvement, we still know little about how gaze features are connected to the physics of gaze. In this paper, we try to answer this question by analyzing the gaze feature manifold. Our analysis revealed the insight that the geodesic distance between gaze features is consistent with the gaze differences between samples. According to this finding, we construct the Physics- Consistent Feature (PCF) in an analytical way, which connects gaze feature to the physical definition of gaze. We further propose the PCFGaze framework that directly optimizes gaze feature space by the guidance of PCF. Experimental results demonstrate that the proposed framework alleviates the overfitting problem and significantly improves cross-domain gaze estimation accuracy without extra training data. The insight of gaze feature has the potential to benefit other regression tasks with physical meanings

Contextual cropping and scaling of TV productions

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0804-3. Copyright @ Springer Science+Business Media, LLC 2011.In this paper, an application is presented which automatically adapts SDTV (Standard Definition Television) sports productions to smaller displays through intelligent cropping and scaling. It crops regions of interest of sports productions based on a smart combination of production metadata and systematic video analysis methods. This approach allows a context-based composition of cropped images. It provides a differentiation between the original SD version of the production and the processed one adapted to the requirements for mobile TV. The system has been comprehensively evaluated by comparing the outcome of the proposed method with manually and statically cropped versions, as well as with non-cropped versions. Envisaged is the integration of the tool in post-production and live workflows

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

Automatic 3D Facial Performance Acquisition and Animation using Monocular Videos

Facial performance capture and animation is an essential component of many applications such as movies, video games, and virtual environments. Video-based facial performance capture is particularly appealing as it offers the lowest cost and the potential use of legacy sources and uncontrolled videos. However, it is also challenging because of complex facial movements at different scales, ambiguity caused by the loss of depth information, and a lack of discernible features on most facial regions. Unknown lighting conditions and camera parameters further complicate the problem. This dissertation explores the video-based 3D facial performance capture systems that use a single video camera, overcome the challenges aforementioned, and produce accurate and robust reconstruction results. We first develop a novel automatic facial feature detection/tracking algorithm that accurately locates important facial features across the entire video sequence, which are then used for 3D pose and facial shape reconstruction. The key idea is to combine the respective powers of local detection, spatial priors for facial feature locations, Active Appearance Models (AAMs), and temporal coherence for facial feature detection. The algorithm runs in realtime and is robust to large pose and expression variations and occlusions. We then present an automatic high-fidelity facial performance capture system that works on monocular videos. It uses the detected facial features along with multilinear facial models to reconstruct 3D head poses and large-scale facial deformation, and uses per-pixel shading cues to add fine-scale surface details such as emerging or disappearing wrinkles and folds. We iterate the reconstruction procedure on large-scale facial geometry and fine-scale facial details to improve the accuracy of facial reconstruction. We further improve the accuracy and efficiency of the large-scale facial performance capture by introducing a local binary feature based 2D feature regression and a convolutional neural network based pose and expression regression, and complement it with an efficient 3D eye gaze tracker to achieve realtime 3D eye gaze animation. We have tested our systems on various monocular videos, demonstrating the accuracy and robustness under a variety of uncontrolled lighting conditions and overcoming significant shape differences across individuals