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Authors: Haoyu Wang ; Changsong Liu and Xiaoqing Ding

Affiliation: Tsinghua University, China

Keyword(s): Still-to-Video Face Recognition, Unconstrained Environment, Regularized Latent Least Squares Regression, Alternating Optimization.

Abstract: In this paper, we present a novel method for the still-to-video face recognition problem in unconstrained environments. Due to variations in head pose, facial expression, lighting condition and image resolution, it is infeasible to directly matching faces from still images and video frames. We regard samples from these two distinct sources as multi-modal or heterogeneous data, and use latent identity vectors in a common subspace to connect two modalities. Differed from the conventional least squares regression problem, unknown latent variables are treated as response to be computed. Besides, several constraint and regularization terms are introduced into the optimization equation. This method is thus called regularized latent least squares regression. We divide the original problem into two sub-problems and develop an alternating optimization algorithm to solve it. Experimental results on two public datasets demonstrate the effectiveness of our method.

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Paper citation in several formats:
Wang, H.; Liu, C. and Ding, X. (2015). Regularized Latent Least Squares Regression for Unconstrained Still-to-Video Face Recognition. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP; ISBN 978-989-758-090-1; ISSN 2184-4321, SciTePress, pages 13-20. DOI: 10.5220/0005267300130020

@conference{visapp15,
author={Haoyu Wang. and Changsong Liu. and Xiaoqing Ding.},
title={Regularized Latent Least Squares Regression for Unconstrained Still-to-Video Face Recognition},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP},
year={2015},
pages={13-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005267300130020},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP
TI - Regularized Latent Least Squares Regression for Unconstrained Still-to-Video Face Recognition
SN - 978-989-758-090-1
IS - 2184-4321
AU - Wang, H.
AU - Liu, C.
AU - Ding, X.
PY - 2015
SP - 13
EP - 20
DO - 10.5220/0005267300130020
PB - SciTePress

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