A BOOSTED MANIFOLD LEARNING FOR AUTOMATIC FACE RECOGNITION
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.
manifold learning face recognition nonlinear dimension reduction discriminating linear form manifold