Head Pose Estimation from Passive Stereo Images

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

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

11 Citationer (Scopus)

Resumé

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%.
OriginalsprogEngelsk
BogserieLecture Notes in Computer Science
Vol/bind5575
Sider (fra-til)219-228
Antal sider9
ISSN0302-9743
DOI
StatusUdgivet - 2009
BegivenhedScandinavian Conference on Image Analysis - Oslo, Norge
Varighed: 15 jun. 200918 jun. 2009
Konferencens nummer: LNCS 5575

Konference

KonferenceScandinavian Conference on Image Analysis
NummerLNCS 5575
LandNorge
ByOslo
Periode15/06/200918/06/2009

Fingerprint

Pose Estimation
Range Image
Error function
Accessories
Signature
Face
Computing
Evaluate
Estimate

Bibliografisk 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

Citer dette

Breitenstein, Michael D. ; Jensen, Jeppe ; Høilund, Carsten ; Moeslund, Thomas B. ; Gool, Luc Van. / Head Pose Estimation from Passive Stereo Images. I: Lecture Notes in Computer Science. 2009 ; Bind 5575. s. 219-228.
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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",
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Head Pose Estimation from Passive Stereo Images. / Breitenstein, Michael D.; Jensen, Jeppe; Høilund, Carsten; Moeslund, Thomas B.; Gool, Luc Van.

I: Lecture Notes in Computer Science, Bind 5575, 2009, s. 219-228.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

TY - GEN

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AU - Gool, Luc Van

N1 - 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

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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%.

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