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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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. 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. In this paper we present a convolutional neural network (CNN)-based model for human head pose estimation in low-resolution multi-modal RGB-D data. We pose the problem as one of classification of human gazing direction. We further fine-tune a regressor based on the learned deep classifier. Next we combine the two models (classification and regression) to estimate approximate regression confidence. We present state-of-the-art results in datasets that span the range of high-resolution human robot interaction (close up faces plus depth information) data to challenging low resolution outdoor surveillance data. We build upon our robust head-pose estimation and further introduce a new visual attention model to recover interaction with the environment . Using this probabilistic model, we show that many higher level scene understanding like human-human scene interaction detection can be achieved. Our solution runs in real-time on commercial hardware. Quality, diversity, and size of training data are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (2M images at 1280x960), and a real-world dataset collected with 35 subjects (2.5M images at 640x480). Using these datasets we train neural networks performing with sub-millisecond latency. Our gaze estimation network achieves 2.06(±0.44)° of accuracy across a wide 30°×40° field of view on real subjects excluded from training and 0.5° best-case accuracy (across the same FOV) when explicitly trained for one real subject. We also train a pupil localization network which achieves higher robustness than previous methods. We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method was tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average accuracy of 4:3°. Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides a sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup, without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators. We present a new method to classify human activities by leveraging on the cues available from depth images alone. Towards this end, we propose a descriptor which couples depth and spatial information of the segmented body to describe a human pose. Unique poses (i.e. codewords) are then identified by a spatial-based clustering step. Given a video sequence of depth images, we segment humans from the depth images and represent these segmented bodies as a sequence of codewords. We exploit unique poses of an activity and the temporal ordering of these poses to learn subsequences of codewords which are strongly discriminative for the activity. Each discriminative subsequence acts as a classifier and we learn a boosted ensemble of discriminative subsequences to assign a confidence score for the activity label of the test sequence. Unlike existing methods which demand accurate tracking of 3D joint locations or couple depth with color image information as recognition cues, our method requires only the segmentation masks from depth images to recognize an activity. Experimental results on the publicly available Human Activity Dataset (which comprises 12 challenging activities) demonstrate the validity of our method, where we attain a precision recall of 78.1 75.4 when the person was not seen before in the training set, and 94.6 93.1 when the person was seen before. Tracking multiple persons in a monocular video of a crowded scene is a challenging task. Humans can master it even if they loose track of a person locally by re-identifying the same person based on their appearance. Care must be taken across long distances, as similar-looking persons need not be identical. In this work, we propose a novel graph-based formulation that links and clusters person hypotheses over time by solving an instance of a minimum cost lifted multicut problem. Our model generalizes previous works by introducing a mechanism for adding long-range attractive connections between nodes in the graph without modifying the original set of feasible solutions. This allows us to reward tracks that assign detections of similar appearance to the same person in a way that does not introduce implausible solutions. To effectively match hypotheses over longer temporal gaps we develop new deep architectures for re-identification of people. They combine holistic representations extracted with deep networks and body pose layout obtained with a state-of-the-art pose estimation model. We demonstrate the effectiveness of our formulation by reporting a new state-of-the-art for the MOT16 benchmark. The code and pre-trained models are publicly available. We present a novel video saliency detection method to support human activity recognition and weakly supervised training of activity detection algorithms. Recent research has emphasized the need for analyzing salient information in videos to minimize dataset bias or to supervise weakly labeled training of activity detectors. In contrast to previous methods we do not rely on training information given by either eye-gaze or annotation data, but propose a fully unsupervised algorithm to find salient regions within videos. In general, we enforce the Gestalt principle of figure-ground segregation for both appearance and motion cues. We introduce an encoding approach that allows for efficient computation of saliency by approximating joint feature distributions. We evaluate our approach on several datasets, including challenging scenarios with cluttered background and camera motion, as well as salient object detection in images. Overall, we demonstrate favorable performance compared to state-of-the-art methods in estimating both ground-truth eye-gaze and activity annotations. Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques. A large amount of training data is usually crucial for successful supervised learning. However, the task of providing training samples is often time-consuming, involving a considerable amount of tedious manual work. In addition, the amount of training data available is often limited. As an alternative, in this paper, we discuss how best to augment the available data for the application of automatic facial landmark detection. We propose the use of a 3D morphable face model to generate synthesized faces for a regression-based detector training. Benefiting from the large synthetic training data, the learned detector is shown to exhibit a better capability to detect the landmarks of a face with pose variations. Furthermore, the synthesized training data set provides accurate and consistent landmarks automatically as compared to the landmarks annotated manually, especially for occluded facial parts. The synthetic data and real data are from different domains; hence the detector trained using only synthesized faces does not generalize well to real faces. To deal with this problem, we propose a cascaded collaborative regression algorithm, which generates a cascaded shape updater that has the ability to overcome the difficulties caused by pose variations, as well as achieving better accuracy when applied to real faces. The training is based on a mix of synthetic and real image data with the mixing controlled by a dynamic mixture weighting schedule. Initially, the training uses heavily the synthetic data, as this can model the gross variations between the various poses. As the training proceeds, progressively more of the natural images are incorporated, as these can model finer detail. To improve the performance of the proposed algorithm further, we designed a dynamic multi-scale local feature extraction method, which captures more informative local features for detector training. An extensive evaluation on both controlled and uncontrolled face data sets demonstrates the merit of the proposed algorithm. 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. 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. Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as self-occlusion, motion blur, and uncommon poses with few or no examples in the training data. Temporal information can provide additional cues about the location of body joints and help to alleviate these issues. In this paper, we propose a deep structured model to estimate a sequence of human poses in unconstrained videos. This model can be efficiently trained in an end-to-end manner and is capable of representing the appearance of body joints and their spatio-temporal relationships simultaneously. Domain knowledge about the human body is explicitly incorporated into the network providing effective priors to regularize the skelet al structure and to enforce temporal consistency. The proposed end-to-end architecture is evaluated on two widely used benchmarks for video-based pose estimation (Penn Action and JHMDB datasets). Our approach outperforms several state-of-the-art methods. 3D model-based gaze estimation methods are widely explored because of their good accuracy and ability to handle free head movement. Traditional methods with complex hardware systems (Eg. infrared lights, 3D sensors, etc.) are restricted to controlled environments, which significantly limit their practical utilities. In this paper, we propose a 3D model-based gaze estimation method with a single web-camera, which enables instant and portable eye gaze tracking. The key idea is to leverage on the proposed 3D eye-face model, from which we can estimate 3D eye gaze from observed 2D facial landmarks. The proposed system includes a 3D deformable eye-face model that is learned offline from multiple training subjects. Given the deformable model, individual 3D eye-face models and personal eye parameters can be recovered through the unified calibration algorithm. Experimental results show that the proposed method outperforms state-of-the-art methods while allowing convenient system setup and free head movement. A real time eye tracking system running at 30 FPS also validates the effectiveness and efficiency of the proposed method. Deep ConvNets have been shown to be effective for the task of human pose estimation from single images. However, several challenging issues arise in the video-based case such as self-occlusion, motion blur, and uncommon poses with few or no examples in training data sets. Temporal information can provide additional cues about the location of body joints and help to alleviate these issues. In this paper, we propose a deep structured model to estimate a sequence of human poses in unconstrained videos. This model can be efficiently trained in an end-to-end manner and is capable of representing appearance of body joints and their spatio-temporal relationships simultaneously. Domain knowledge about the human body is explicitly incorporated into the network providing effective priors to regularize the skelet al structure and to enforce temporal consistency. The proposed end-to-end architecture is evaluated on two widely used benchmarks (Penn Action dataset and JHMDB dataset) for video-based pose estimation. Our approach significantly outperforms the existing state-of-the-art methods. We propose a probabilistic formulation of joint silhouette extraction and 3D reconstruction given a series of calibrated 2D images. Instead of segmenting each image separately in order to construct a 3D surface consistent with the estimated silhouettes, we compute the most probable 3D shape that gives rise to the observed color information. The probabilistic framework, based on Bayesian inference, enables robust 3D reconstruction by optimally taking into account the contribution of all views. We solve the arising maximum a posteriori shape inference in a globally optimal manner by convex relaxation techniques in a spatially continuous representation. For an interactively provided user input in the form of scribbles specifying foreground and background regions, we build corresponding color distributions as multivariate Gaussians and find a volume occupancy that best fits to this data in a variational sense. Compared to classical methods for silhouette-based multiview reconstruction, the proposed approach does not depend on initialization and enjoys significant resilience to violations of the model assumptions due to background clutter, specular reflections, and camera sensor perturbations. In experiments on several real-world data sets, we show that exploiting a silhouette coherency criterion in a multiview setting allows for dramatic improvements of silhouette quality over independent 2D segmentations without any significant increase of computational efforts. This results in more accurate visual hull estimation, needed by a multitude of image-based modeling approaches. We made use of recent advances in parallel computing with a GPU implementation of the proposed method generating reconstructions on volume grids of more than 20 million voxels in up to 4.41 seconds. We address the problem of person independent 3D gaze estimation using a remote, low resolution, RGB-D camera. The approach relies on a sparse technique to reconstruct normalized eye test images from a gaze appearance model (a set of eye image gaze pairs) and infer their gaze accordingly. In this context, the paper makes three contributions: (i) unlike most previous approaches, we exploit the coupling (and constraints) between both eyes to infer their gaze jointly; (ii) we show that a generic gaze appearance model built from the aggregation of person-specific models can be used to handle unseen users and compensate for appearance variations across people, since a test user eyes' appearance will be reconstructed from similar users within the generic model. (iii) we propose an automatic model selection method that leads to comparable performance with a reduced computational load. Our ability to train end-to-end systems for 3D human pose estimation from single images is currently constrained by the limited availability of 3D annotations for natural images. Most datasets are captured using Motion Capture (MoCap) systems in a studio setting and it is difficult to reach the variability of 2D human pose datasets, like MPII or LSP. To alleviate the need for accurate 3D ground truth, we propose to use a weaker supervision signal provided by the ordinal depths of human joints. This information can be acquired by human annotators for a wide range of images and poses. We showcase the effectiveness and flexibility of training Convolutional Networks (ConvNets) with these ordinal relations in different settings, always achieving competitive performance with ConvNets trained with accurate 3D joint coordinates. Additionally, to demonstrate the potential of the approach, we augment the popular LSP and MPII datasets with ordinal depth annotations. This extension allows us to present quantitative and qualitative evaluation in non-studio conditions. Simultaneously, these ordinal annotations can be easily incorporated in the training procedure of typical ConvNets for 3D human pose. Through this inclusion we achieve new state-of-the-art performance for the relevant benchmarks and validate the effectiveness of ordinal depth supervision for 3D human pose. In this paper, we address the problem of free head motion in appearance-based gaze estimation. This problem remains challenging because head motion changes eye appearance significantly, and thus, training images captured for an original head pose cannot handle test images captured for other head poses. To overcome this difficulty, we propose a novel gaze estimation method that handles free head motion via eye image synthesis based on a single camera. Compared with conventional fixed head pose methods with original training images, our method only captures four additional eye images under four reference head poses, and then, precisely synthesizes new training images for other unseen head poses in estimation. To this end, we propose a single-directional (SD) flow model to efficiently handle eye image variations due to head motion. We show how to estimate SD flows for reference head poses first, and then use them to produce new SD flows for training image synthesis. Finally, with synthetic training images, joint optimization is applied that simultaneously solves an eye image alignment and a gaze estimation. Evaluation of the method was conducted through experiments to assess its performance and demonstrate its effectiveness. In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.