Camera distance from face images

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

Standard

Camera distance from face images. / Flores, Arturo; Christiansen, Eric; Kriegman, David; Belongie, Serge.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. PART 2, 2013, p. 513-522.

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

Harvard

Flores, A, Christiansen, E, Kriegman, D & Belongie, S 2013, 'Camera distance from face images', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, pp. 513-522. https://doi.org/10.1007/978-3-642-41939-3_50

APA

Flores, A., Christiansen, E., Kriegman, D., & Belongie, S. (2013). Camera distance from face images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), (PART 2), 513-522. https://doi.org/10.1007/978-3-642-41939-3_50

Vancouver

Flores A, Christiansen E, Kriegman D, Belongie S. Camera distance from face images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013;(PART 2):513-522. https://doi.org/10.1007/978-3-642-41939-3_50

Author

Flores, Arturo ; Christiansen, Eric ; Kriegman, David ; Belongie, Serge. / Camera distance from face images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013 ; No. PART 2. pp. 513-522.

Bibtex

@inproceedings{5d8b5657363c492ab2ef2b0ec8967870,
title = "Camera distance from face images",
abstract = "We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, and roll) and translations perpendicular to the camera principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera's principal axis. Our method uses a set of exemplar 3D human heads to estimate the distance between a camera and a previously unseen head. The distance is estimated by solving for the camera pose using Effective Perspective n-Point (EPnP). We present promising experimental results using the Texas 3D Face Recognition Database.",
author = "Arturo Flores and Eric Christiansen and David Kriegman and Serge Belongie",
year = "2013",
doi = "10.1007/978-3-642-41939-3_50",
language = "English",
pages = "513--522",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",
number = "PART 2",
note = "9th International Symposium on Advances in Visual Computing, ISVC 2013 ; Conference date: 29-07-2013 Through 31-07-2013",

}

RIS

TY - GEN

T1 - Camera distance from face images

AU - Flores, Arturo

AU - Christiansen, Eric

AU - Kriegman, David

AU - Belongie, Serge

PY - 2013

Y1 - 2013

N2 - We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, and roll) and translations perpendicular to the camera principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera's principal axis. Our method uses a set of exemplar 3D human heads to estimate the distance between a camera and a previously unseen head. The distance is estimated by solving for the camera pose using Effective Perspective n-Point (EPnP). We present promising experimental results using the Texas 3D Face Recognition Database.

AB - We present a method for estimating the distance between a camera and a human head in 2D images from a calibrated camera. Leading head pose estimation algorithms focus mainly on head orientation (yaw, pitch, and roll) and translations perpendicular to the camera principal axis. Our contribution is a system that can estimate head pose under large translations parallel to the camera's principal axis. Our method uses a set of exemplar 3D human heads to estimate the distance between a camera and a previously unseen head. The distance is estimated by solving for the camera pose using Effective Perspective n-Point (EPnP). We present promising experimental results using the Texas 3D Face Recognition Database.

UR - http://www.scopus.com/inward/record.url?scp=84888260266&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-41939-3_50

DO - 10.1007/978-3-642-41939-3_50

M3 - Conference article

AN - SCOPUS:84888260266

SP - 513

EP - 522

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

IS - PART 2

T2 - 9th International Symposium on Advances in Visual Computing, ISVC 2013

Y2 - 29 July 2013 through 31 July 2013

ER -

ID: 302046971