A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Incremental semi-supervised subspace learning for image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Semi-supervised nonlinear dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised classification by local coordination
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Hi-index | 0.00 |
Face expression analysis and recognition play an important role in human face emotion perception and social interaction and have therefore attracted much attention in recent years. Semi-Supervised manifold learning has been successfully applied to facial expression recognition by modeling different expressions as a smooth manifold embedded in a high dimensional space. However, the best classification accuracy does not necessarily guarantee as the assumption of double manifold is still arguable. In this paper, we study a family of semi-supervised learning algorithms for aligning different data sets that are characterzied by the same underlying manifold. The generalized framework for modeling and recognizing facial expressions on multiple manifolds is presented. First, we introduce an assumption of one expression one manifold for facial expression recognition. Second, we propose a feasible algorithm for multiple manifold based facial expression recognition. Extensive experiments show the effectiveness of the proposed approach.