Appearance-Based Gaze Estimation in the Wild

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

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning

We propose a novel neural pipeline, MSGazeNet, that learns gaze representations by taking advantage of the eye anatomy information through a multistream framework. Our proposed solution comprises two components, first a network for isolating anatomical eye regions, and a second network for multistream gaze estimation. The eye region isolation is performed with a U-Net style network which we train using a synthetic dataset that contains eye region masks for the visible eyeball and the iris region. The synthetic dataset used in this stage is procured using the UnityEyes simulator, and consists of 80,000 eye images. Successive to training, the eye region isolation network is then transferred to the real domain for generating masks for the real-world eye images. In order to successfully make the transfer, we exploit domain randomization in the training process, which allows for the synthetic images to benefit from a larger variance with the help of augmentations that resemble artifacts. The generated eye region masks along with the raw eye images are then used together as a multistream input to our gaze estimation network, which consists of wide residual blocks. The output embeddings from these encoders are fused in the channel dimension before feeding into the gaze regression layers. We evaluate our framework on three gaze estimation datasets and achieve strong performances. Our method surpasses the state-of-the-art by 7.57% and 1.85% on two datasets, and obtains competitive results on the other. We also study the robustness of our method with respect to the noise in the data and demonstrate that our model is less sensitive to noisy data. Lastly, we perform a variety of experiments including ablation studies to evaluate the contribution of different components and design choices in our solution.Comment: 15 pages, 7 figures, 14 tables. This work has been accepted to the IEEE Transactions on Artificial Intelligence $\copyright$ 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other use

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

Building a Self-Learning Eye Gaze Model from User Interaction Data

2014 ACM Conference on Multimedia, MM 2014, 3-7 November 2014Most 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 4.06°, and demonstrates the effectiveness of the proposed gaze estimation method and corresponding validation mechanism.Department of ComputingRefereed conference pape

Gaze estimation and interaction in real-world environments

Human eye gaze has been widely used in human-computer interaction, as it is a promising modality for natural, fast, pervasive, and non-verbal interaction between humans and computers. As the foundation of gaze-related interactions, gaze estimation has been a hot research topic in recent decades. In this thesis, we focus on developing appearance-based gaze estimation methods and corresponding attentive user interfaces with a single webcam for challenging real-world environments. First, we collect a large-scale gaze estimation dataset, MPIIGaze, the first of its kind, outside of controlled laboratory conditions. Second, we propose an appearance-based method that, in stark contrast to a long-standing tradition in gaze estimation, only takes the full face image as input. Second, we propose an appearance-based method that, in stark contrast to a long-standing tradition in gaze estimation, only takes the full face image as input. Third, we study data normalisation for the first time in a principled way, and propose a modification that yields significant performance improvements. Fourth, we contribute an unsupervised detector for human-human and human-object eye contact. Finally, we study personal gaze estimation with multiple personal devices, such as mobile phones, tablets, and laptops.Der Blick des menschlichen Auges wird in Mensch-Computer-Interaktionen verbreitet eingesetzt, da dies eine vielversprechende Möglichkeit für natürliche, schnelle, allgegenwärtige und nonverbale Interaktion zwischen Mensch und Computer ist. Als Grundlage von blickbezogenen Interaktionen ist die Blickschätzung in den letzten Jahrzehnten ein wichtiges Forschungsthema geworden. In dieser Arbeit konzentrieren wir uns auf die Entwicklung Erscheinungsbild-basierter Methoden zur Blickschätzung und entsprechender “attentive user interfaces” (die Aufmerksamkeit des Benutzers einbeziehende Benutzerschnittstellen) mit nur einer Webcam für anspruchsvolle natürliche Umgebungen. Zunächst sammeln wir einen umfangreichen Datensatz zur Blickschätzung, MPIIGaze, der erste, der außerhalb von kontrollierten Laborbedingungen erstellt wurde. Zweitens schlagen wir eine Erscheinungsbild-basierte Methode vor, die im Gegensatz zur langjährigen Tradition in der Blickschätzung nur eine vollständige Aufnahme des Gesichtes als Eingabe verwendet. Drittens untersuchen wir die Datennormalisierung erstmals grundsätzlich und schlagen eine Modifizierung vor, die zu signifikanten Leistungsverbesserungen führt. Viertens stellen wir einen unüberwachten Detektor für Augenkontakte zwischen Mensch und Mensch und zwischen Mensch und Objekt vor. Abschließend untersuchen wir die persönliche Blickschätzung mit mehreren persönlichen Geräten wie Handy, Tablet und Laptop