A novel non-intrusive eye gaze estimation using cross-ratio under large head motion

Yoo, Dong Hyun; Chung, Myung Jin

doi:10.1016/j.cviu.2004.07.011

Cited by 202 publications

(110 citation statements)

References 10 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…The proposed method obviously achieves higher accuracy and requires much less calibration effort. In fact, the average estimation accuracy of 2.38 • is comparable to the feature-based methods [3,9,15,19,20], which commonly report accuracies of 1 ∼ 3 • by utilizing complex devices such as infrared/stereo cameras/lights and pan-tilt units.…”

Section: Overall Assessment Of Estimation Accuracy Under Free Head Momentioning

confidence: 53%

See 1 more Smart Citation

A Head Pose-free Approach for Appearance-based Gaze Estimation

Okabe

Sugano

et al. 2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

To infer human gaze from eye appearance, various methods have been proposed. However, most of them assume a fixed head pose because allowing free head motion adds 6 degrees of freedom to the problem and requires a prohibitively large number of training samples. In this paper, we aim at solving the appearance-based gaze estimation problem under free head motion without significantly increasing the cost of training. The idea is to decompose the problem into subproblems, including initial estimation under fixed head pose and subsequent compensations for estimation biases caused by head rotation and eye appearance distortion. Then each subproblem is solved by either learning-based method or geometric-based calculation. Specifically, the gaze estimation bias caused by eye appearance distortion is learnt effectively from a 5-seconds video clip. Extensive experiments were conducted to verify the effectiveness of the proposed approach.

show abstract

Section: Overall Assessment Of Estimation Accuracy Under Free Head Momentioning

confidence: 53%

“…Chung [19] proposed a novel method based on cross-ratio that avoids the explicit computation of the 3-D positions of the eye, cameras, and screen. Kang et al [5] further improved this method by considering the differences between individual eye parameters.…”

Section: Introductionmentioning

confidence: 99%

A Head Pose-free Approach for Appearance-based Gaze Estimation

Okabe

Sugano

et al. 2011

Procedings of the British Machine Vision Conference 2011

View full text Add to dashboard Cite

show abstract

“…This model-based method is further divided into the corneal-reflection-based method and shape-based method. The corneal-reflection-based gaze tracking uses NIR camera and NIR illumination to calculate the gaze from the relationship between the change in the pupil's position and the position of light reflection in the cornea [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The shape-based method detects the pupil or iris contour, and as the shape of the pupil or iris contour becomes elliptical according to the gaze direction, it predicts the corresponding shape of a three-dimensional (3D) spherical eye model and calculates the gaze [24].…”

Section: Related Workmentioning

confidence: 99%

Fuzzy-System-Based Detection of Pupil Center and Corneal Specular Reflection for a Driver-Gaze Tracking System Based on the Symmetrical Characteristics of Face and Facial Feature Points

Lee¹,

Yoon²,

Hong³

et al. 2017

Symmetry

View full text Add to dashboard Cite

Abstract:Recently, many studies have actively dealt with the issue of driver-gaze tracking for monitoring the forward gaze and physical condition. Driver-gaze tracking is an effective method of measuring a driver's inattention that is one of the major causes of traffic accidents. Among many gaze-tracking methods, the corneal specular reflection (SR)-based method becomes ineffective, unlike in an indoor environment, when a driver's head rotates, which makes SR disappear from input images or disperses SR in the lachrymal gland or eyelid, thereby increasing the gaze-tracking error. Besides, since a driver's eyes in a vehicle environment need to be captured in a wide range covering his head rotation, the eye region is captured in a relatively low resolution compared to face-only images taken in indoor environments at the same resolution, making pupil and corneal SR difficult to detect accurately. To solve these problems, we propose a fuzzy-system-based method for detecting a driver's pupil and corneal SR for gaze tracking in a vehicle environment. Unlike existing studies detecting pupil and corneal SR in both eyes, the method proposed in this research uses the results of a fuzzy system based on two features considering the symmetrical characteristics of face and facial feature points to determine the status of a driver's head rotation. Based on the output of the fuzzy system, the proposed method excludes the eye region, which is very likely to have a high error rate of detection due to excessive head rotation, from the detection process of the pupil and corneal SR. Accordingly, the proposed method detects pupil and corneal SR only in the eye region that apparently has a low detection error rate, thereby achieving accurate detection. We use 20,654 images capturing 15 subjects (including subjects wearing glasses), who gaze at pre-set fifteen regions in a vehicle, to measure the detection accuracy of the pupil and corneal SR for each region and the gaze tracking accuracy. Our experimental results show that the proposed method performs better than existing methods.

show abstract

“…Other approaches which are not constrained by specific hardware, referred to as 'non-intrusive' systems, have to compensate for motion affects of the user (e.g. by using tracking methods [Baluja and Pomerleau, 1994, Ji and Zhu, 2003, Zhu and Ji, 2005, Yoo and Chung, 2005), though they still employ active sensing tools (e.g. by illuminating the eyes with infrared light).…”

Section: Previous Work On Eye Gaze Estimationmentioning

confidence: 99%

Detection of Head Pose and Gaze Direction for Human-Computer Interaction

Weidenbacher

Layher

Bayerl

et al. 2006

Perception and Interactive Technologies

View full text Add to dashboard Cite

Abstract. In this contribution we extend existing methods for head pose estimation and investigate the use of local image phase for gaze detection. Moreover we describe how a small database of face images with given ground truth for head pose and gaze direction was acquired. With this database we compare two different computational approaches for extracting the head pose. We demonstrate that a simple implementation of the proposed methods without extensive training sessions or calibration is sufficient to accurately detect the head pose for human-computer interaction. Furthermore, we propose how eye gaze can be extracted based on the outcome of local filter responses and the detected head pose. In all, we present a framework where different approaches are combined to a single system for extracting information about the attentional state of a person.

show abstract

A novel non-intrusive eye gaze estimation using cross-ratio under large head motion

Cited by 202 publications

References 10 publications

A Head Pose-free Approach for Appearance-based Gaze Estimation

A Head Pose-free Approach for Appearance-based Gaze Estimation

Fuzzy-System-Based Detection of Pupil Center and Corneal Specular Reflection for a Driver-Gaze Tracking System Based on the Symmetrical Characteristics of Face and Facial Feature Points

Detection of Head Pose and Gaze Direction for Human-Computer Interaction

Contact Info

Product

Resources

About