Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
The requirement to collect person-specific training data during a calibration step is a key limitation of both model-based and appearance-based methods. To address this limitation, several previous works used interaction events, such as mouse clicks or key presses, as a proxy for the user's on-screen gaze position @cite_45 @cite_46 . Alternatively, visual saliency maps @cite_29 @cite_8 or pre-recorded human gaze patterns of the presented visual stimuli @cite_50 were used as bottom-up, probabilistic training data to learn the estimation function. However, all of these approaches rely on observations of a specific person and environment, which limits their applicability.
@cite_8 @cite_29 @cite_45 @cite_50 @cite_46
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Preface 1. Introduction 2. Some context for Context Analysis: a view of the origins of structural studies of face-to-face interaction 3. Some functions of gaze direction in two-person conversation 4. Movement co-ordination in social interaction 5. Some functions of the face in a kissing round 6. A description of some human greetings 7. Spatial organisation in social encounters: the F-formation system 8. Behavioural foundations for the process of frame-attunement in face-to-face interaction List of films cited References Index. Existing eye gaze tracking systems typically require an explicit personal calibration process in order to estimate certain person-specific eye parameters. For natural human computer interaction, such a personal calibration is often cumbersome and unnatural. In this paper, we propose a new probabilistic eye gaze tracking system without explicit personal calibration. Unlike the traditional eye gaze tracking methods, which estimate the eye parameter deterministically, our approach estimates the probability distributions of the eye parameter and the eye gaze, by combining image saliency with the 3D eye model. By using an incremental learning framework, the subject doesn't need personal calibration before using the system. His her eye parameter and gaze estimation can be improved gradually when he she is naturally viewing a sequence of images on the screen. The experimental result shows that the proposed system can achieve less than three degrees accuracy for different people without calibration. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interaction (HCI) community. This is partly because it remains unclear how they perform compared to model-based approaches as well as dominant, special-purpose eye tracking equipment. To address this limitation, we evaluate the performance of state-of-the-art appearance-based gaze estimation for interaction scenarios with and without personal calibration, indoors and outdoors, for different sensing distances, as well as for users with and without glasses. We discuss the obtained findings and their implications for the most important gaze-based applications, namely explicit eye input, attentive user interfaces, gaze-based user modelling, and passive eye monitoring. To democratise the use of appearance-based gaze estimation and interaction in HCI, we finally present OpenGaze (this http URL), the first software toolkit for appearance-based gaze estimation and interaction. Most eye gaze estimation systems rely on explicit calibration, which is inconvenient to the user, limits the amount of possible training data and consequently the performance. Since there is likely a strong correlation between gaze and interaction cues, such as cursor and caret locations, a supervised learning algorithm can learn the complex mapping between gaze features and the gaze point by training on incremental data collected implicitly from normal computer interactions. We develop a set of robust geometric gaze features and a corresponding data validation mechanism that identifies good training data from noisy interaction-informed data collected in real-use scenarios. Based on a study of gaze movement patterns, we apply behavior-informed validation to extract gaze features that correspond with the interaction cue, and data-driven validation provides another level of crosschecking using previous good data. Experimental evaluation shows that the proposed method achieves an average error of 4.06o, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism. Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi-view gaze dataset and perform a 3D reconstruction in order to generate dense training data of eye images. By using the synthesized dataset to learn a random regression forest, we show that our method outperforms existing methods that use low-resolution eye images. We propose a method of predicting human egocentric visual attention using bottom-up visual saliency and egomotion information. Computational models of visual saliency are often employed to predict human attention; however, its mechanism and effectiveness have not been fully explored in egocentric vision. The purpose of our framework is to compute attention maps from an egocentric video that can be used to infer a person's visual attention. In addition to a standard visual saliency model, two kinds of attention maps are computed based on a camera's rotation velocity and direction of movement. These rotation-based and translation-based attention maps are aggregated with a bottom-up saliency map to enhance the accuracy with which the person's gaze positions can be predicted. The efficiency of the proposed framework was examined in real environments by using a head-mounted gaze tracker, and we found that the egomotion-based attention maps contributed to accurately predicting human visual attention. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Stress sensing is valuable in many applications, including online learning crowdsourcing and other daily human-computer interactions. Traditional affective computing techniques investigate affect inference based on different individual modalities, such as facial expression, vocal tones, and physiological signals or the aggregation of signals of these independent modalities, without explicitly exploiting their inter-connections. In contrast, this paper focuses on exploring the impact of mental stress on the coordination between two human nervous systems, the somatic and autonomic nervous systems. Specifically, we present the analysis of the subtle but indicative pattern of human gaze behaviors surrounding a mouse-click event, i.e. the gaze-click pattern. Our evaluation shows that mental stress affects the gaze-click pattern, and this influence has largely been ignored in previous work. This paper, therefore, further proposes a non-intrusive approach to inferring human stress level based on the gaze-click pattern, using only data collected from the common computer webcam and mouse. We conducted a human study on solving math questions under different stress levels to explore the validity of stress recognition based on this coordination pattern. Experimental results show the effectiveness of our technique and the generalizability of the proposed features for user-independent modeling. Our results suggest that it may be possible to detect stress non-intrusively in the wild, without the need for specialized equipment. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . Following the gaze of people inside videos is an important signal for understanding people and their actions. In this paper, we present an approach for following gaze in video by predicting where a person (in the video) is looking even when the object is in a different frame. We collect VideoGaze, a new dataset which we use as a benchmark to both train and evaluate models. Given one frame with a person in it, our model estimates a density for gaze location in every frame and the probability that the person is looking in that particular frame. A key aspect of our approach is an end-to-end model that jointly estimates: saliency, gaze pose, and geometric relationships between views while only using gaze as supervision. Visualizations suggest that the model learns to internally solve these intermediate tasks automatically without additional supervision. Experiments show that our approach follows gaze in video better than existing approaches, enabling a richer understanding of human activities in video. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc. We propose a gaze sensing method using visual saliency maps that does not need explicit personal calibration. Our goal is to create a gaze estimator using only the eye images captured from a person watching a video clip. Our method treats the saliency maps of the video frames as the probability distributions of the gaze points. We aggregate the saliency maps based on the similarity in eye images to efficiently identify the gaze points from the saliency maps. We establish a mapping between the eye images to the gaze points by using Gaussian process regression. In addition, we use a feedback loop from the gaze estimator to refine the gaze probability maps to improve the accuracy of the gaze estimation. The experimental results show that the proposed method works well with different people and video clips and achieves a 3.5-degree accuracy, which is sufficient for estimating a user's attention on a display. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Eye contact plays a crucial role in our everyday social interactions. The ability of a device to reliably detect when a person is looking at it can lead to powerful human-object interfaces. Today, most gaze-based interactive systems rely on gaze tracking technology. Unfortunately, current gaze tracking techniques require active infrared illumination, calibration, or are sensitive to distance and pose. In this work, we propose a different solution-a passive, appearance-based approach for sensing eye contact in an image. By focusing on gaze *locking* rather than gaze tracking, we exploit the special appearance of direct eye gaze, achieving a Matthews correlation coefficient (MCC) of over 0.83 at long distances (up to 18 m) and large pose variations (up to ±30° of head yaw rotation) using a very basic classifier and without calibration. To train our detector, we also created a large publicly available gaze data set: 5,880 images of 56 people over varying gaze directions and head poses. We demonstrate how our method facilitates human-object interaction, user analytics, image filtering, and gaze-triggered photography. Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze that contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves the state of the art by 22 percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation. We introduce WebGazer, an online eye tracker that uses common webcams already present in laptops and mobile devices to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model self-calibrates by watching web visitors interact with the web page and trains a mapping between features of the eye and positions on the screen. This approach aims to provide a natural experience to everyday users that is not restricted to laboratories and highly controlled user studies. WebGazer has two key components: a pupil detector that can be combined with any eye detection library, and a gaze estimator using regression analysis informed by user interactions. We perform a large remote online study and a small in-person study to evaluate WebGazer. The findings show that WebGazer can learn from user interactions and that its accuracy is sufficient for approximating the user's gaze. As part of this paper, we release the first eye tracking library that can be easily integrated in any website for real-time gaze interactions, usability studies, or web research. Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22 percent (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.