Showing papers on "Eye tracking published in 2020"

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines

Abstract: Visual tracking problem demands to efficiently perform robust classification and accurate target state estimation over a given target at the same time. Former methods have proposed various ways of target state estimation, yet few of them took the particularity of the visual tracking problem itself into consideration. Based on a careful analysis, we propose a set of practical guidelines of target state estimation for high-performance generic object tracker design. Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch (G1), classification score without ambiguity (G2), tracking without prior knowledge (G3), and estimation quality score (G4). Extensive analysis and ablation studies demonstrate the effectiveness of our proposed guidelines. Without bells and whistles, our SiamFC++ tracker achieves state-of-the-art performance on five challenging benchmarks(OTB2015, VOT2018, LaSOT, GOT-10k, TrackingNet), which proves both the tracking and generalization ability of the tracker. Particularly, on the large-scale TrackingNet dataset, SiamFC++ achieves a previously unseen AUC score of 75.4 while running at over 90 FPS, which is far above the real-time requirement.

Overview and methods of correlation filter algorithms in object tracking

Abstract: An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.

Probabilistic Regression for Visual Tracking

Abstract: Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore crucial to represent the uncertainty in the target estimation. Although current prominent paradigms rely on estimating a state-dependent confidence score, this value lacks a clear probabilistic interpretation, complicating its use. In this work, we therefore propose a probabilistic regression formulation and apply it to tracking. Our network predicts the conditional probability density of the target state given an input image. Crucially, our formulation is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task. The regression network is trained by minimizing the Kullback-Leibler divergence. When applied for tracking, our formulation not only allows a probabilistic representation of the output, but also substantially improves the performance. Our tracker sets a new state-of-the-art on six datasets, achieving 59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are available at this https URL.

Spatial and semantic convolutional features for robust visual object tracking

Abstract: Robust and accurate visual tracking is a challenging problem in computer vision. In this paper, we exploit spatial and semantic convolutional features extracted from convolutional neural networks in continuous object tracking. The spatial features retain higher resolution for precise localization and semantic features capture more semantic information and less fine-grained spatial details. Therefore, we localize the target by fusing these different features, which improves the tracking accuracy. Besides, we construct the multi-scale pyramid correlation filter of the target and extract its spatial features. This filter determines the scale level effectively and tackles target scale estimation. Finally, we further present a novel model updating strategy, and exploit peak sidelobe ratio (PSR) and skewness to measure the comprehensive fluctuation of response map for efficient tracking performance. Each contribution above is validated on 50 image sequences of tracking benchmark OTB-2013. The experimental comparison shows that our algorithm performs favorably against 12 state-of-the-art trackers.

A User Study on Mixed Reality Remote Collaboration with Eye Gaze and Hand Gesture Sharing

Abstract: Supporting natural communication cues is critical for people to work together remotely and face-to-face. In this paper we present a Mixed Reality (MR) remote collaboration system that enables a local worker to share a live 3D panorama of his/her surroundings with a remote expert. The remote expert can also share task instructions back to the local worker using visual cues in addition to verbal communication. We conducted a user study to investigate how sharing augmented gaze and gesture cues from the remote expert to the local worker could affect the overall collaboration performance and user experience. We found that by combing gaze and gesture cues, our remote collaboration system could provide a significantly stronger sense of co-presence for both the local and remote users than using the gaze cue alone. The combined cues were also rated significantly higher than the gaze in terms of ease of conveying spatial actions.

Gaze Estimation by Exploring Two-Eye Asymmetry

Abstract: Eye gaze estimation is increasingly demanded by recent intelligent systems to facilitate a range of interactive applications. Unfortunately, learning the highly complicated regression from a single eye image to the gaze direction is not trivial. Thus, the problem is yet to be solved efficiently. Inspired by the two-eye asymmetry as two eyes of the same person may appear uneven, we propose the face-based asymmetric regression-evaluation network (FARE-Net) to optimize the gaze estimation results by considering the difference between left and right eyes. The proposed method includes one face-based asymmetric regression network (FAR-Net) and one evaluation network (E-Net). The FAR-Net predicts 3D gaze directions for both eyes and is trained with the asymmetric mechanism, which asymmetrically weights and sums the loss generated by two-eye gaze directions. With the asymmetric mechanism, the FAR-Net utilizes the eyes that can achieve high performance to optimize network. The E-Net learns the reliabilities of two eyes to balance the learning of the asymmetric mechanism and symmetric mechanism. Our FARE-Net achieves leading performances on MPIIGaze, EyeDiap and RT-Gene datasets. Additionally, we investigate the effectiveness of FARE-Net by analyzing the distribution of errors and ablation study.

Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies

Abstract: Recent advancements in electronic packaging and image processing techniques have opened the possibility for optics-based portable eye tracking approaches, but technical and safety hurdles limit safe implementation toward wearable applications. Here, we introduce a fully wearable, wireless soft electronic system that offers a portable, highly sensitive tracking of eye movements (vergence) via the combination of skin-conformal sensors and a virtual reality system. Advancement of material processing and printing technologies based on aerosol jet printing enables reliable manufacturing of skin-like sensors, while the flexible hybrid circuit based on elastomer and chip integration allows comfortable integration with a user's head. Analytical and computational study of a data classification algorithm provides a highly accurate tool for real-time detection and classification of ocular motions. In vivo demonstration with 14 human subjects captures the potential of the wearable electronics as a portable therapy system, whose minimized form factor facilitates seamless interplay with traditional wearable hardware.

High-Performance Long-Term Tracking With Meta-Updater

Abstract: Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus,they cannot benefit from great progress of short-term trackers with online update. However, it is quite risky to straightforwardly introduce online-update-based trackers to solve the long-term problem, due to long-term uncertain and noisy observations. In this work, we propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame? The proposed meta-updater can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module. Our meta-updater learns a binary output to guide the tracker’s update and can be easily embedded into different trackers. This work also introduces a long-term tracking framework consisting of an online local tracker, an online verifier, a SiamRPN-based re-detector, and our meta-updater. Numerous experimental results on the VOT2018LT,VOT2019LT, OxUvALT, TLP, and LaSOT benchmarks show that our tracker performs remarkably better than other competing algorithms. Our project is available on the website: https://github.com/Daikenan/LTMU.

Siamese attentional keypoint network for high performance visual tracking

Abstract: Visual tracking is one of the most fundamental topics in computer vision. Numerous tracking approaches based on discriminative correlation filters or Siamese convolutional networks have attained remarkable performance over the past decade. However, it is still commonly recognized as an open research problem to develop robust and effective trackers which can achieve satisfying performance with high computational and memory storage efficiency in real-world scenarios. In this paper, we investigate the impacts of three main aspects of visual tracking, i.e., the backbone network, the attentional mechanism, and the detection component, and propose a Siamese Attentional Keypoint Network, dubbed SATIN, for efficient tracking and accurate localization. Firstly, a new Siamese lightweight hourglass network is specially designed for visual tracking. It takes advantage of the benefits of the repeated bottom-up and top-down inference to capture more global and local contextual information at multiple scales. Secondly, a novel cross-attentional module is utilized to leverage both channel-wise and spatial intermediate attentional information, which can enhance both discriminative and localization capabilities of feature maps. Thirdly, a keypoints detection approach is invented to trace any target object by detecting the top-left corner point, the centroid point, and the bottom-right corner point of its bounding box. Therefore, our SATIN tracker not only has a strong capability to learn more effective object representations, but also is computational and memory storage efficiency, either during the training or testing stages. To the best of our knowledge, we are the first to propose this approach. Without bells and whistles, experimental results demonstrate that our approach achieves state-of-the-art performance on several recent benchmark datasets, at a speed far exceeding 27 frames per second.

High-Performance Long-Term Tracking with Meta-Updater

Abstract: Long-term visual tracking has drawn increasing attention because it is much closer to practical applications than short-term tracking. Most top-ranked long-term trackers adopt the offline-trained Siamese architectures, thus, they cannot benefit from great progress of short-term trackers with online update. However, it is quite risky to straightforwardly introduce online-update-based trackers to solve the long-term problem, due to long-term uncertain and noisy observations. In this work, we propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame? The proposed meta-updater can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module. Our meta-updater learns a binary output to guide the tracker's update and can be easily embedded into different trackers. This work also introduces a long-term tracking framework consisting of an online local tracker, an online verifier, a SiamRPN-based re-detector, and our meta-updater. Numerous experimental results on the VOT2018LT, VOT2019LT, OxUvALT, TLP, and LaSOT benchmarks show that our tracker performs remarkably better than other competing algorithms. Our project is available on the website: this https URL.

Accelerating eye movement research via accurate and affordable smartphone eye tracking.

Abstract: Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.

The impact of slippage on the data quality of head-worn eye trackers

Abstract: Mobile head-worn eye trackers allow researchers to record eye-movement data as participants freely move around and interact with their surroundings. However, participant behavior may cause the eye tracker to slip on the participant’s head, potentially strongly affecting data quality. To investigate how this eye-tracker slippage affects data quality, we designed experiments in which participants mimic behaviors that can cause a mobile eye tracker to move. Specifically, we investigated data quality when participants speak, make facial expressions, and move the eye tracker. Four head-worn eye-tracking setups were used: (i) Tobii Pro Glasses 2 in 50 Hz mode, (ii) SMI Eye Tracking Glasses 2.0 60 Hz, (iii) Pupil-Labs’ Pupil in 3D mode, and (iv) Pupil-Labs’ Pupil with the Grip gaze estimation algorithm as implemented in the EyeRecToo software. Our results show that whereas gaze estimates of the Tobii and Grip remained stable when the eye tracker moved, the other systems exhibited significant errors (0.8–3.1∘ increase in gaze deviation over baseline) even for the small amounts of glasses movement that occurred during the speech and facial expressions tasks. We conclude that some of the tested eye-tracking setups may not be suitable for investigating gaze behavior when high accuracy is required, such as during face-to-face interaction scenarios. We recommend that users of mobile head-worn eye trackers perform similar tests with their setups to become aware of its characteristics. This will enable researchers to design experiments that are robust to the limitations of their particular eye-tracking setup.

The Role of Eye Gaze in Security and Privacy Applications: Survey and Future HCI Research Directions

Abstract: For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.

Does visual attention to the instructor in online video affect learning and learner perceptions? An eye-tracking analysis

Abstract: An increasing number of instructional videos online integrate a real instructor on the video screen. So far, the empirical evidence from previous studies has been limited and conflicting, and none of the studies have explored how learners' allocation of visual attention to the on-screen instructor influences learning and learner perceptions. Therefore, this study aimed to disentangle a) how instructor presence in online videos affects learning, learner perceptions (i.e., cognitive load, judgment of learning, satisfaction, situational interest), and visual attention distribution and b) to what extent visual attention patterns in instructor-present videos predict learning and learner perceptions. Sixty college students each watched two videos on Statistics, one on an easy topic and the other one on a difficult topic, with each in one of the two video formats: instructor-present or instructor-absent. Their eye movements were simultaneously registered using a desktop-mounted eye tracker. Afterwards, participants self-reported their cognitive load, judgment of learning, satisfaction, and situational interest for both videos, and feelings toward seeing the instructor for the instructor-present videos. Learning from the two videos was measured using retention and transfer questions. Findings indicated instructor presence a) improved transfer performance for the difficult topic, b) reduced cognitive load for the difficult topic, c) increased judgment of learning for the difficult topic, and d) enhanced satisfaction and situational interest for both topics. Most participants expressed a positive feeling toward the instructor. Results also showed the instructor attracted a considerable amount of overt visual attention in both videos, and the amount of attention allocated to the instructor positively predicted participants’ satisfaction level for both topics.

How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks

Abstract: The traffic driving environment is a complex and dynamic changing scene in which drivers have to pay close attention to salient and important targets or regions for safe driving. Modeling drivers’ eye movements and attention allocation in traffic driving can also help guiding unmanned intelligent vehicles. However, until now, few studies have modeled drivers’ true fixations and allocations while driving. To this end, we collect an eye tracking dataset from a total of 28 experienced drivers viewing 16 traffic driving videos. Based on the multiple drivers’ attention allocation dataset, we propose a convolutional-deconvolutional neural network (CDNN) to predict the drivers’ eye fixations. The experimental results indicate that the proposed CDNN outperforms the state-of-the-art saliency models and predicts drivers’ attentional locations more accurately. The proposed CDNN can predict the major fixation location and shows excellent detection of secondary important information or regions that cannot be ignored during driving if they exist. Compared with the present object detection models in autonomous and assisted driving systems, our human-like driving model does not detect all of the objects appearing in the driving scenes, but it provides the most relevant regions or targets, which can largely reduce the interference of irrelevant scene information.

Eye-tracking methodology in mathematics education research: A systematic literature review

Abstract: Eye tracking is an increasingly popular method in mathematics education. While the technology has greatly evolved in recent years, there is a debate about the specific benefits that eye tracking offers and about the kinds of insights it may allow. The aim of this review is to contribute to this discussion by providing a comprehensive overview of the use of eye tracking in mathematics education research. We reviewed 161 eye-tracking studies published between 1921 and 2018 to assess what domains and topics were addressed, how the method was used, and how eye movements were related to mathematical thinking and learning. The results show that most studies were in the domain of numbers and arithmetic, but that a large variety of other areas of mathematics education research was investigated as well. We identify a need to report more methodological details in eye-tracking studies and to be more critical about how to gather, analyze, and interpret eye-tracking data. In conclusion, eye tracking seemed particularly beneficial for studying processes rather than outcomes, for revealing mental representations, and for assessing subconscious aspects of mathematical thinking.

Robust visual tracking with correlation filters and metric learning

Abstract: Discriminative correlation filters (DCFs) have been widely used in the visual tracking community in recent years. The DCFs-based trackers determine the target location through a response map generated by the correlation filters and determine the target scale by a fixed scale factor. However, the response map is vulnerable to noise interference and the fixed scale factor also cannot reflect the real scale change of the target, which can obviously reduce the tracking performance. In this paper, to solve the aforementioned drawbacks, we propose to learn a metric learning model in correlation filters framework for visual tracking (called CFML). This model can use a metric learning function to solve the target scale problem. In particular, we adopt a hard negative mining strategy to alleviate the influence of the noise on the response map, which can effectively improve the tracking accuracy. Extensive experimental results demonstrate that the proposed CFML tracker achieves competitive performance compared with the state-of-the-art trackers.

Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities.

Abstract: The study of gaze behavior has primarily been constrained to controlled environments in which the head is fixed. Consequently, little effort has been invested in the development of algorithms for the categorization of gaze events (e.g. fixations, pursuits, saccade, gaze shifts) while the head is free, and thus contributes to the velocity signals upon which classification algorithms typically operate. Our approach was to collect a novel, naturalistic, and multimodal dataset of eye + head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera. This Gaze-in-the-Wild dataset (GW) includes eye + head rotational velocities (deg/s), infrared eye images and scene imagery (RGB + D). A portion was labelled by coders into gaze motion events with a mutual agreement of 0.74 sample based Cohen’s κ. This labelled data was used to train and evaluate two machine learning algorithms, Random Forest and a Recurrent Neural Network model, for gaze event classification. Assessment involved the application of established and novel event based performance metrics. Classifiers achieve ~87% human performance in detecting fixations and saccades but fall short (50%) on detecting pursuit movements. Moreover, pursuit classification is far worse in the absence of head movement information. A subsequent analysis of feature significance in our best performing model revealed that classification can be done using only the magnitudes of eye and head movements, potentially removing the need for calibration between the head and eye tracking systems. The GW dataset, trained classifiers and evaluation metrics will be made publicly available with the intention of facilitating growth in the emerging area of head-free gaze event classification.

Eye-Tracking Metrics Predict Perceived Workload in Robotic Surgical Skills Training:

Abstract: ObjectiveThe aim of this study is to assess the relationship between eye-tracking measures and perceived workload in robotic surgical tasks.BackgroundRobotic techniques provide improved dexterity, ...

What do we see out of the corner of our eye? The role of visual pivots and gaze anchors in sport

Abstract: The role and importance of peripheral vision have been discussed across various sports. Yet, its functionality remains unclear and the terms employed in the literature to characterize the use of pe...

Bi-directional navigation intent communication using spatial augmented reality and eye-tracking glasses for improved safety in human–robot interaction

Abstract: Safety, legibility and efficiency are essential for autonomous mobile robots that interact with humans. A key factor in this respect is bi-directional communication of navigation intent, which we focus on in this article with a particular view on industrial logistic applications. In the direction robot-to-human, we study how a robot can communicate its navigation intent using Spatial Augmented Reality (SAR) such that humans can intuitively understand the robot’s intention and feel safe in the vicinity of robots. We conducted experiments with an autonomous forklift that projects various patterns on the shared floor space to convey its navigation intentions. We analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift and carried out stimulated recall interviews (SRI) in order to identify desirable features for projection of robot intentions. In the direction human-to-robot, we argue that robots in human co-habited environments need human-aware task and motion planning to support safety and efficiency, ideally responding to people’s motion intentions as soon as they can be inferred from human cues. Eye gaze can convey information about intentions beyond what can be inferred from the trajectory and head pose of a person. Hence, we propose eye-tracking glasses as safety equipment in industrial environments shared by humans and robots. In this work, we investigate the possibility of human-to-robot implicit intention transference solely from eye gaze data and evaluate how the observed eye gaze patterns of the participants relate to their navigation decisions. We again analyzed trajectories and eye gaze patterns of humans while interacting with an autonomous forklift for clues that could reveal direction intent. Our analysis shows that people primarily gazed on that side of the robot they ultimately decided to pass by. We discuss implications of these results and relate to a control approach that uses human gaze for early obstacle avoidance.

A practical guide on conducting eye tracking studies in software engineering

Abstract: For several years, the software engineering research community used eye trackers to study program comprehension, bug localization, pair programming, and other software engineering tasks. Eye trackers provide researchers with insights on software engineers’ cognitive processes, data that can augment those acquired through other means, such as on-line surveys and questionnaires. While there are many ways to take advantage of eye trackers, advancing their use requires defining standards for experimental design, execution, and reporting. We begin by presenting the foundations of eye tracking to provide context and perspective. Based on previous surveys of eye tracking for programming and software engineering tasks and our collective, extensive experience with eye trackers, we discuss when and why researchers should use eye trackers as well as how they should use them. We compile a list of typical use cases—real and anticipated—of eye trackers, as well as metrics, visualizations, and statistical analyses to analyze and report eye-tracking data. We also discuss the pragmatics of eye tracking studies. Finally, we offer lessons learned about using eye trackers to study software engineering tasks. This paper is intended to be a one-stop resource for researchers interested in designing, executing, and reporting eye tracking studies of software engineering tasks.

Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

Abstract: How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives w...

Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning

Abstract: The purpose of this study is to test whether visual processing differences between adults with and without high-functioning autism captured through eye tracking can be used to detect autism. We record the eye movements of adult participants with and without autism while they look for information within web pages. We then use the recorded eye-tracking data to train machine learning classifiers to detect the condition. The data was collected as part of two separate studies involving a total of 71 unique participants (31 with autism and 40 control), which enabled the evaluation of the approach on two separate groups of participants, using different stimuli and tasks. We explore the effects of a number of gaze-based and other variables, showing that autism can be detected automatically with around 74% accuracy. These results confirm that eye-tracking data can be used for the automatic detection of high-functioning autism in adults and that visual processing differences between the two groups exist when processing web pages.

DGaze: CNN-Based Gaze Prediction in Dynamic Scenes

Abstract: We conduct novel analyses of users' gaze behaviors in dynamic virtual scenes and, based on our analyses, we present a novel CNN-based model called DGaze for gaze prediction in HMD-based applications. We first collect 43 users' eye tracking data in 5 dynamic scenes under free-viewing conditions. Next, we perform statistical analysis of our data and observe that dynamic object positions, head rotation velocities, and salient regions are correlated with users' gaze positions. Based on our analysis, we present a CNN-based model (DGaze) that combines object position sequence, head velocity sequence, and saliency features to predict users' gaze positions. Our model can be applied to predict not only realtime gaze positions but also gaze positions in the near future and can achieve better performance than prior method. In terms of realtime prediction, DGaze achieves a 22.0% improvement over prior method in dynamic scenes and obtains an improvement of 9.5% in static scenes, based on using the angular distance as the evaluation metric. We also propose a variant of our model called DGaze_ET that can be used to predict future gaze positions with higher precision by combining accurate past gaze data gathered using an eye tracker. We further analyze our CNN architecture and verify the effectiveness of each component in our model. We apply DGaze to gaze-contingent rendering and a game, and also present the evaluation results from a user study.

Eye movement characteristics in schizophrenia: A recent update with clinical implications.

Abstract: Eye movements are indispensable for the collection of visual information in everyday life. Many findings regarding the neural basis of eye movements have been accumulated from neurophysiological and psychophysical studies. In the field of psychiatry, studies on eye movement characteristics in mental illnesses have been conducted since the early 1900s. Participants with schizophrenia are known to have characteristic eye movements during smooth pursuit, saccade control, and visual search. Recently, studies evaluating eye movement characteristics as biomarkers for schizophrenia have attracted considerable attention. In this article, we review the neurophysiological basis of eye movement control and eye movement characteristics in schizophrenia. Furthermore, we discuss the prospects for eye movements as biomarkers for mental illnesses.