A boosted manifold learning for automatic face recognition

Lu, C, Jiang, J and Feng, G (2010) A boosted manifold learning for automatic face recognition International Journal of Pattern Recognition and Artificial Intelligence, 24 (2). pp. 321-335.

Full text not available from this repository.

Abstract

Manifold learning is an effective dimension reduction method to extract nonlinear structures from high dimensional data. Recently, manifold learning has received much attention within the research communities of image analysis, computer vision and document data analysis. In this paper, we propose a boosted manifold learning algorithm towards automatic 2D face recognition by using AdaBoost to select the best possible discriminating projection for manifold learning to exploit the strength of both techniques. Experimental results support that the proposed algorithm improves over existing benchmarks in terms of stability and recognition precision rates.

Item Type:	Article
Authors :	Name Email ORCID Lu, C Jiang, J jianmin.jiang@surrey.ac.uk Feng, G
Date :	2010
Identification Number :	10.1142/S0218001410007919
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 12:25
Last Modified :	17 May 2017 15:03
URI:	http://epubs.surrey.ac.uk/id/eprint/835249

Actions (login required)

View Item

Downloads