Face Recognition with Image Sets Using Manifold Density Divergence
- 1 January 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (10636919) , 581-588
- https://doi.org/10.1109/cvpr.2005.151
Abstract
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.Keywords
This publication has 17 references indexed in Scilit:
- Robust Real-Time Face DetectionInternational Journal of Computer Vision, 2004
- Face recognition based on fitting a 3D morphable modelPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Probabilistic recognition of human faces from videoComputer Vision and Image Understanding, 2003
- Beyond eigenfaces: probabilistic matching for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- A morphable model for the synthesis of 3D facesPublished by Association for Computing Machinery (ACM) ,1999
- The minimum description length principle in coding and modelingIEEE Transactions on Information Theory, 1998
- Face recognition: the problem of compensating for changes in illumination directionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Eigenfaces vs. Fisherfaces: recognition using class specific linear projectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1997
- Visual learning and recognition of 3-d objects from appearanceInternational Journal of Computer Vision, 1995
- Human Face Recognition and the Face Image Set's TopologyComputer Vision and Image Understanding, 1994