Manifold-Manifold Distance with application to face recognition based on image set

By Ruiping Wang, Shiguang Shan, Xilin Chen, Wen Gao. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

445 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/d17a954a3d04ac8f887ec01fcadc8aa42a9cff01

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Jiwen Lu
3. Dacheng Tao
4. Xilin Chen
5. G. Wang
6. Shiguang Shan
7. Xiaogang Wang
8. Zhiwu Huang

In Media Technology

1. Zhiwu Huang
2. Jiwen Lu
3. Shiguang Shan
4. Xilin Chen
5. Ruiping Wang
6. Mehrtash Harandi
7. Yap-Peng Tan
8. G. Wang

Citations in the corpus

• Log-Euclidean Metric Learning on Symmetric Positive Definite Manifold with Application to Image Set Classification (2015, methodology): “Over the years, a surge of methods (Wang et al., 2008; Wang & Chen, 2009; Cevikalp & Triggs, 2010; Hu et al., Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015.”
• Deep Reconstruction Models for Image Set Classification (2015, methodology): “Image set classification methods commonly model the appearance variability information within images of a set on a geometric surface such as an image set modeled by a subspace [4], [15], a combination of subspaces [5], [16], a point on the Grassmannian [8] or Lie Group [10] of Riemannian manifold.”
• Discriminative multi-manifold analysis for face recognition from a single training sample per person (2011, methodology): “For example, Wang et al. [30] proposed a maximal linear patch (MLP) clustering method to partition each manifold into several clusters, modeled each cluster with a subspace, and then computed the nearest sub-space distance as the manifold distance.”
• Manifold Discriminant Analysis (2009, methodology): “In our earlier work [26], a novel local linear model called maximal linear patch (MLP), was introduced directly from the view of manifold.”
• Face recognition in unconstrained videos with matched background similarity (2011, methodology): “Different choices of similarity measures are then used to compare two sets [19, 20, 24].”
• Graph embedding discriminant analysis on Grassmannian manifolds for improved image set matching (2011, background): “A convenient way of dealing with subspaces is to represent them as points on Grassmannian manifolds [11, 13, 19, 25].”
• Covariance discriminative learning: A natural and efficient approach to image set classification (2012, methodology): “However, in the general scenario of image set classification, images in a set are collected not necessarily from video sequences but possibly from multiple unordered observations [10], [13], [26], so that the images suitable for recognition are widely spaced in time and location.”
• Discriminative Deep Metric Learning for Face Verification in the Wild (2014, methodology): “These methods are Discriminant-analysis of Canonical Correlations (DCC) [17], Manifold-Manifold Distance (MMD) [33], Affine Hull based Image Set Distance (AHISD) [5], Convex Hull based Image Set Distance (CHISD) [5], and Sparse Approximated Nearest Points (SANP) [12].”