Face shape recovery from a single image using CCA mapping between tensor spaces

In this paper, we propose a new approach for face shape recovery from a single image. A single near infrared (NIR) image is used as the input, and a mapping from the NIR tensor space to 3D tensor space, learned by using statistical learning, is used for the shape recovery. In the learning phase, the...

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Bibliographic Details
Published in:2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 7
Main Authors: Zhen Lei, Qinqun Bai, Ran He, Li, S.Z.
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2008
Subjects:
Face detection
Humans
Image reconstruction
Infrared imaging
Lighting
Magnetic force microscopy
Shape
Surface fitting
Surface reconstruction
Tensile stress
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary:In this paper, we propose a new approach for face shape recovery from a single image. A single near infrared (NIR) image is used as the input, and a mapping from the NIR tensor space to 3D tensor space, learned by using statistical learning, is used for the shape recovery. In the learning phase, the two tensor models are constructed for NIR and 3D images respectively, and a canonical correlation analysis (CCA) based multi-variate mapping from NIR to 3D faces is learned from a given training set of NIR-3D face pairs. In the reconstruction phase, given an NIR face image, the depth map is computed directly using the learned mapping with the help of tensor models. Experimental results are provided to evaluate the accuracy and speed of the method. The work provides a practical solution for reliable and fast shape recovery and modeling of 3D objects.
ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587341