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.