Name
Abstract | ||
|---|---|---|
This paper presents a method to model and recognize human faces in video sequences. Each registered person is represented by a low-dimensional appearance manifold in the ambient image space, the complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), and the connectivity among them. Each pose manifold is approximated by an affine plane. To construct this representation, exemplars are sampled from videos, and these exemplars are clustered with a K-means algorithm; each cluster is represented as a plane computed through principal component analysis (PCA). The connectivity between the pose manifolds encodes the transition probability between images in each of the pose manifold and is learned from a training video sequences. A maximum a posteriori formulation is presented for face recognition in test video sequences by integrating the likelihood that the input image comes from a particular pose manifold and the transition probability to this pose manifold from the previous frame. To recognize faces with partial occlusion, we introduce a weight mask into the process. Extensive experiments demonstrate that the proposed algorithm outperforms existing frame-based face recognition methods with temporal voting schemes. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/CVPR.2003.1211369 | CVPR (1) |
Keywords | Field | DocType |
video signal processing,test video sequence,image representation,low-dimensional appearance manifold,face recognition,transition probability,exemplar,learning (artificial intelligence),pose manifold,weight masking,k-means algorithm,ambient image space,maximum a posteriori formulation,affine plane,transition matrix,image sequence,image sequences,pca,probabilistic appearance manifold,training video sequence,video-based recognition,face modeling,principal component analysis,complex nonlinear appearance manifold,video-based face recognition,probability,video sequence,learning artificial intelligence,computer science,clustering algorithms,testing,k means algorithm,head,image recognition,manifolds | Affine transformation,k-means clustering,Facial recognition system,Computer vision,Pattern recognition,Computer science,Manifold alignment,Artificial intelligence,Probabilistic logic,Maximum a posteriori estimation,Cluster analysis,Manifold | Conference |
Volume | ISSN | ISBN |
1 | 1063-6919 | 0-7695-1900-8 |
Citations | PageRank | References |
247 | 12.43 | 33 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kuang-chih Lee | 1 | 2297 | 104.80 |
| Jeffrey Ho | 2 | 2190 | 101.78 |
| Yang Ming-Hsuan | 3 | 15303 | 620.69 |
| David Kriegman | 4 | 7693 | 451.96 |