Practical gaze point computing method by 3D position estimation of facial and eye features

Kang Ryoung Park

doi:10.1007/978-3-540-30549-1_22

Practical gaze point computing method by 3D position estimation of facial and eye features

Kang Ryoung Park

Department of Electronics & Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper addresses the accurate gaze detection method by tracking facial and eye movement at the same time. For that, we implemented our gaze detection system with a wide and a narrow view stereo camera. In order to make it easier to detect the facial and eye feature positions, the dual IR-LED illuminators are also used for our system. The performance of detecting facial features could be enhanced by Support Vector Machine and the eye gaze position on a monitor is computed by a multi-layered perceptron. Experimental results show that the RMS error of gaze detection is about 2.4 degrees (1.68 degrees on X axis and 1.71 degrees on Y axis at the Z distance of 50 cm).

Original language	English
Pages (from-to)	237-247
Number of pages	11
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3339
DOIs	https://doi.org/10.1007/978-3-540-30549-1_22
State	Published - 2004
Event	17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence - Cairns, Australia Duration: 4 Dec 2004 → 6 Dec 2004

Keywords

Facial and Eye Movement
Gaze Detection

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10.1007/978-3-540-30549-1_22

Cite this

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title = "Practical gaze point computing method by 3D position estimation of facial and eye features",

abstract = "This paper addresses the accurate gaze detection method by tracking facial and eye movement at the same time. For that, we implemented our gaze detection system with a wide and a narrow view stereo camera. In order to make it easier to detect the facial and eye feature positions, the dual IR-LED illuminators are also used for our system. The performance of detecting facial features could be enhanced by Support Vector Machine and the eye gaze position on a monitor is computed by a multi-layered perceptron. Experimental results show that the RMS error of gaze detection is about 2.4 degrees (1.68 degrees on X axis and 1.71 degrees on Y axis at the Z distance of 50 cm).",

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T1 - Practical gaze point computing method by 3D position estimation of facial and eye features

AU - Park, Kang Ryoung

PY - 2004

Y1 - 2004

N2 - This paper addresses the accurate gaze detection method by tracking facial and eye movement at the same time. For that, we implemented our gaze detection system with a wide and a narrow view stereo camera. In order to make it easier to detect the facial and eye feature positions, the dual IR-LED illuminators are also used for our system. The performance of detecting facial features could be enhanced by Support Vector Machine and the eye gaze position on a monitor is computed by a multi-layered perceptron. Experimental results show that the RMS error of gaze detection is about 2.4 degrees (1.68 degrees on X axis and 1.71 degrees on Y axis at the Z distance of 50 cm).

AB - This paper addresses the accurate gaze detection method by tracking facial and eye movement at the same time. For that, we implemented our gaze detection system with a wide and a narrow view stereo camera. In order to make it easier to detect the facial and eye feature positions, the dual IR-LED illuminators are also used for our system. The performance of detecting facial features could be enhanced by Support Vector Machine and the eye gaze position on a monitor is computed by a multi-layered perceptron. Experimental results show that the RMS error of gaze detection is about 2.4 degrees (1.68 degrees on X axis and 1.71 degrees on Y axis at the Z distance of 50 cm).

KW - Facial and Eye Movement

KW - Gaze Detection

UR - https://www.scopus.com/pages/publications/22944444856

U2 - 10.1007/978-3-540-30549-1_22

DO - 10.1007/978-3-540-30549-1_22

M3 - Conference article

AN - SCOPUS:22944444856

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EP - 247

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 17th Australian Joint Conference on Artificial Intelligence, AI 2004: Advances in Artificial Intelligence

Y2 - 4 December 2004 through 6 December 2004

ER -

Practical gaze point computing method by 3D position estimation of facial and eye features

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Keywords

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