Name
Abstract | ||
|---|---|---|
In this paper, we propose a probabilistic video-based facial expression recognition method on manifolds. The concept of the manifold of facial expression is based on the observation that the images of all possible facial deformations of an individual make a smooth manifold embedded in a high dimensional image space. An enhanced Lipschitz embedding is developed to embed the aligned face appearance in a low dimensional space while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. Each path consists of several clusters. A probabilistic model of transition between the clusters and paths is learned through training videos in the embedded space. The likelihood of one kind of facial expression is modeled as a mixture density with the clusters as mixture centers. The transition between different expressions is represented as the evolution of the posterior probability of the six basic paths. The experimental results demonstrate that the probabilistic approach can recognize expression transitions effectively. We also synthesize image sequences of changing expressions through the manifold model. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/CVPR.2004.1315208 | CVPR (2) |
Keywords | Field | DocType |
probabilistic video-based facial expression recognition,high dimensional image space,face recognition,manifolds,neutral expression,facial deformations,facial expression recognition method,complete expression sequence,embedded space,different expression,manifold model,facial expression,emotion recognition,image sequences,smooth manifold,probabilistic expression analysis,expression manifold,image space,probability,probabilistic model,posterior probability | Computer vision,Facial recognition system,Embedding,Pattern recognition,Expression (mathematics),Computer science,Posterior probability,Facial expression,Lipschitz continuity,Artificial intelligence,Probabilistic logic,Manifold | Conference |
Volume | ISSN | ISBN |
2 | 1063-6919 | 0-7695-2158-4 |
Citations | PageRank | References |
80 | 4.62 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ya Chang | 1 | 321 | 15.68 |
| Changbo Hu | 2 | 613 | 34.71 |
| Matthew Turk | 3 | 3724 | 499.42 |