Face recognition with image sets using manifold density divergence
Contribuinte(s) |
[Unknown] |
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Data(s) |
01/01/2005
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Resumo |
In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face recognition using image sets. We propose a flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average. |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
http://dro.deakin.edu.au/eserv/DU:30058434/arandjelovic-facerecognitionwith-2005.pdf http://doi.org/10.1109/CVPR.2005.151 |
Direitos |
2005, IEEE |
Tipo |
Conference Paper |