Regularized Latent Least Squares Regression for Unconstrained Still-to-Video Face Recognition

Haoyu Wang, Changsong Liu, Xiaoqing Ding

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 Harvard Style

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 - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 13-20. DOI: 10.5220/0005267300130020


in Bibtex Style

@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 - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={13-20},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005267300130020},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Regularized Latent Least Squares Regression for Unconstrained Still-to-Video Face Recognition
SN - 978-989-758-090-1
AU - Wang H.
AU - Liu C.
AU - Ding X.
PY - 2015
SP - 13
EP - 20
DO - 10.5220/0005267300130020