Epipolar Constraint from 2D Affine Lines, and Its Application in Face Image Rendering

Kuntal SENGUPTA; Jun OHYA

Epipolar Constraint from 2D Affine Lines, and Its Application in Face Image Rendering

Kuntal SENGUPTA, Jun OHYA

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This paper has two parts. In the first part of the paper, we note the property that under the para perspective camera projection model of a camera, the set of 2D images produced by a 3D point can be optimally represented by two lines in the affine space (α-β space). The slope of these two lines are same, and we observe that this constraint is exactly the same as the epipolar line constraint. Using this constraint, the equation of the epipolar line can be derived. In the second part of the paper, we use the "same slope" property of the lines in the α-β space to derive the affine structure of the human face. The input to the algorithm is not limited to an image sequence of a human head under rigid motion. It can be snapshots of the human face taken by the same or different cameras, over different periods of time. Since the depth variation of the human face is not very large, we use the para perspective camera projection model. Using this property, we reformulate the (human) face structure reconstruction problem in terms of the much familiar multiple baseline stereo matching problem. Apart from the face modeling aspect, we also show how we use the results for reprojecting human faces in identification tasks.

Publication: IEICE TRANSACTIONS on Information Vol.E83-D No.7 pp.1567-1573

Publication Date: 2000/07/25

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Type of Manuscript: PAPER

Category: Image Processing, Image Pattern Recognition

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Kuntal SENGUPTA, Jun OHYA, "Epipolar Constraint from 2D Affine Lines, and Its Application in Face Image Rendering" in IEICE TRANSACTIONS on Information, vol. E83-D, no. 7, pp. 1567-1573, July 2000, doi: .
Abstract: This paper has two parts. In the first part of the paper, we note the property that under the para perspective camera projection model of a camera, the set of 2D images produced by a 3D point can be optimally represented by two lines in the affine space (α-β space). The slope of these two lines are same, and we observe that this constraint is exactly the same as the epipolar line constraint. Using this constraint, the equation of the epipolar line can be derived. In the second part of the paper, we use the "same slope" property of the lines in the α-β space to derive the affine structure of the human face. The input to the algorithm is not limited to an image sequence of a human head under rigid motion. It can be snapshots of the human face taken by the same or different cameras, over different periods of time. Since the depth variation of the human face is not very large, we use the para perspective camera projection model. Using this property, we reformulate the (human) face structure reconstruction problem in terms of the much familiar multiple baseline stereo matching problem. Apart from the face modeling aspect, we also show how we use the results for reprojecting human faces in identification tasks.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_7_1567/_p

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@ARTICLE{e83-d_7_1567,
author={Kuntal SENGUPTA, Jun OHYA, },
journal={IEICE TRANSACTIONS on Information},
title={Epipolar Constraint from 2D Affine Lines, and Its Application in Face Image Rendering},
year={2000},
volume={E83-D},
number={7},
pages={1567-1573},
abstract={This paper has two parts. In the first part of the paper, we note the property that under the para perspective camera projection model of a camera, the set of 2D images produced by a 3D point can be optimally represented by two lines in the affine space (α-β space). The slope of these two lines are same, and we observe that this constraint is exactly the same as the epipolar line constraint. Using this constraint, the equation of the epipolar line can be derived. In the second part of the paper, we use the "same slope" property of the lines in the α-β space to derive the affine structure of the human face. The input to the algorithm is not limited to an image sequence of a human head under rigid motion. It can be snapshots of the human face taken by the same or different cameras, over different periods of time. Since the depth variation of the human face is not very large, we use the para perspective camera projection model. Using this property, we reformulate the (human) face structure reconstruction problem in terms of the much familiar multiple baseline stereo matching problem. Apart from the face modeling aspect, we also show how we use the results for reprojecting human faces in identification tasks.},
keywords={},
doi={},
ISSN={},
month={July},}

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TY - JOUR
TI - Epipolar Constraint from 2D Affine Lines, and Its Application in Face Image Rendering
T2 - IEICE TRANSACTIONS on Information
SP - 1567
EP - 1573
AU - Kuntal SENGUPTA
AU - Jun OHYA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2000
AB - This paper has two parts. In the first part of the paper, we note the property that under the para perspective camera projection model of a camera, the set of 2D images produced by a 3D point can be optimally represented by two lines in the affine space (α-β space). The slope of these two lines are same, and we observe that this constraint is exactly the same as the epipolar line constraint. Using this constraint, the equation of the epipolar line can be derived. In the second part of the paper, we use the "same slope" property of the lines in the α-β space to derive the affine structure of the human face. The input to the algorithm is not limited to an image sequence of a human head under rigid motion. It can be snapshots of the human face taken by the same or different cameras, over different periods of time. Since the depth variation of the human face is not very large, we use the para perspective camera projection model. Using this property, we reformulate the (human) face structure reconstruction problem in terms of the much familiar multiple baseline stereo matching problem. Apart from the face modeling aspect, we also show how we use the results for reprojecting human faces in identification tasks.
ER -