Manifold Alignment for Person Independent Appearance-Based Gaze Estimation

Schneider, Timo; Schauerte, Boris; Stiefelhagen, Rainer

doi:10.1109/icpr.2014.210

Cited by 65 publications

(47 citation statements)

References 20 publications

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“…The systems described in [36][37][38][39][40] learn the gaze direction by modeling the corresponding eye appearance. In the work of Schneider et al [40] several regression techniques are evaluated, modeling the appearance of the eyes (in terms of features such as Histogram of Oriented Gradients and Local Binary Patters) when gazing at different directions, in order to build a calibration-free system. Hansen et al [41] use an active appearance model of the eye using shape and texture properties to be used with an eye typing interface.…”

Section: Gaze Estimation Methodologiesmentioning

confidence: 99%

On visual gaze tracking based on a single low cost camera

Skodras

Kanas

Fakotakis

2015

Signal Processing: Image Communication

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Section: Gaze Estimation Methodologiesmentioning

confidence: 99%

On visual gaze tracking based on a single low cost camera

Skodras

Kanas

Fakotakis

2015

Signal Processing: Image Communication

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“…Schneider el al. [18] proposed to use manifold alignment for person-independent gaze estimation. However, these appearance-based methods all assumed a fixed head pose.…”

Section: Introductionmentioning

confidence: 99%

Remote gaze estimation based on 3D face structure and iris centers under natural light

Xiong

Huang

Liu

2015

Multimed Tools Appl

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Remote gaze estimation under natural light is still a challenging problem. Appearance based methods are seriously sensitive to illumination variation in the visual spectrum and usually can hardly handle the problem of head movements. And most existing feature-based gaze estimation methods strongly rely on cornea reflections, which are unstable to glasses, head movements and especially useless for natural light condition. In this paper, we propose a novel feature based gaze estimation method without use of cornea reflections. A stereo camera system is built for the proposed method. Firstly, 3D Active Shape Models (ASM) is reconstructed using stereo vision to represent 3D face structure. Then, without use of cornea reflections, a 3D Iris-Eye-Contours based descriptor is proposed to represent human gaze information. Iris centers are used in natural light just like the pupil centers in condition of near-infrared light. What's more, precise estimation of head poses based on 3D face structure is employed to rectify the 3D iris centers and eye contours for improving the ability of tolerance to head movements. Experiments on several subjects show that the system is accurate and allows natural head movements under natural light.

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“…These techniques require head and pupil position estimation in order to track gaze accurately. Most of these approaches require hardware configurations to obtain head pose invariance with most of them to be feature or appearance based, [10,24,25,19,22,18,12].…”

Section: Introductionmentioning

confidence: 99%