Face Recognition Based on Discriminative Manifold Learning

Authors:
Yiming Wu;Kap Luk Chan;Lei Wang
Affiliations:
Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore
Venue:
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Year:
2004

Citing 0
Cited 6

Using locally estimated geodesic distance to optimize neighborhood graph for isometric data embedding

Pattern Recognition
Learning video manifold for segmenting crowd events and abnormality detection

ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
A novel gait recognition analysis system based on body sensor networks for patients with Parkinson's disease

International Journal of Communication Networks and Distributed Systems
Neighborhood dependent approximation by nonlinear embedding for face recognition

ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Recent advances in subspace analysis for face recognition

SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
A local tangent space alignment based transductive classification algorithm

ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a discriminative manifold learning method for face recognition is proposed which achieved the discriminative embedding the high dimensional face data into a low dimensional hidden manifold. Unlike the recently proposed LLE, Isomap and Eigenmap algorithms, which are based on reconstruction purpose, our method use the RCA algorithm to achieve nonlinear embedding and data discrimination at the same time. Also, the LLE and Isomap algorithms are crucially depends on the appropriateness of the neighborhood construction rule, in this paper, a CK-nearest neighborhood rule is proposed to achieve better neighborhood construction. Experimental results indicate the promising performance of the proposed method.