Head Pose Estimation from Passive Stereo Images

Michael D. Breitenstein; Jeppe Jensen; Carsten Høilund; Thomas B. Moeslund; Luc Van Gool

doi:10.1007/978-3-642-02230-2_23

Head Pose Estimation from Passive Stereo Images

Michael D. Breitenstein, Jeppe Jensen, Carsten Høilund, Thomas B. Moeslund, Luc Van Gool

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

14 Citations (Scopus)

Abstract

We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30°, the system achieves an accuracy of 83.6%.

Original language	English
Book series	Lecture Notes in Computer Science
Volume	5575
Pages (from-to)	219-228
Number of pages	9
ISSN	0302-9743
DOIs	https://doi.org/10.1007/978-3-642-02230-2_23
Publication status	Published - 2009
Event	Scandinavian Conference on Image Analysis - Oslo, Norway Duration: 15 Jun 2009 → 18 Jun 2009 Conference number: LNCS 5575

Conference

Conference	Scandinavian Conference on Image Analysis
Number	LNCS 5575
Country/Territory	Norway
City	Oslo
Period	15/06/2009 → 18/06/2009

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title = "Head Pose Estimation from Passive Stereo Images",

abstract = "We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30°, the system achieves an accuracy of 83.6%.",

author = "Breitenstein, {Michael D.} and Jeppe Jensen and Carsten H{\o}ilund and Moeslund, {Thomas B.} and Gool, {Luc Van}",

note = "Titel: Proceedings of the 16th Scandinavian Conference on Image Analysis. Oversat titel: Oversat undertitel: Forlag: Springer ISBN (Trykt): 9783642022296 ISBN (Elektronisk): Publikationsserier: Lecture Notes In Computer Science, Springer, 0302-9743 Image Processing, Computer Vision, Pattern Recognition, and Graphics, Springer, 5575 ; Scandinavian Conference on Image Analysis ; Conference date: 15-06-2009 Through 18-06-2009",

year = "2009",

doi = "10.1007/978-3-642-02230-2_23",

language = "English",

volume = "5575",

pages = "219--228",

journal = "Lecture Notes in Computer Science",

issn = "0302-9743",

publisher = "Physica-Verlag",

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T1 - Head Pose Estimation from Passive Stereo Images

AU - Breitenstein, Michael D.

AU - Jensen, Jeppe

AU - Høilund, Carsten

AU - Moeslund, Thomas B.

AU - Gool, Luc Van

N1 - Conference code: LNCS 5575

PY - 2009

Y1 - 2009

N2 - We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30°, the system achieves an accuracy of 83.6%.

AB - We present an algorithm to estimate the 3D pose (location and orientation) of a previously unseen face from low-quality range images. The algorithm generates many pose candidates from a signature to find the nose tip based on local shape, and then evaluates each candidate by computing an error function. Our algorithm incorporates 2D and 3D cues to make the system robust to low-quality range images acquired by passive stereo systems. It handles large pose variations (of ±90 ° yaw and ±45 ° pitch rotation) and facial variations due to expressions or accessories. For a maximally allowed error of 30°, the system achieves an accuracy of 83.6%.

U2 - 10.1007/978-3-642-02230-2_23

DO - 10.1007/978-3-642-02230-2_23

M3 - Conference article in Journal

SN - 0302-9743

VL - 5575

SP - 219

EP - 228

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

T2 - Scandinavian Conference on Image Analysis

Y2 - 15 June 2009 through 18 June 2009

ER -

Head Pose Estimation from Passive Stereo Images

Abstract

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