Face Recognition Based on Semi-supervised Manifold Learning
Feng Hai-liang · 2008
Recently,manifold learning and semi-supervised learning are two hot topics in the field of machine learning.However,there are only a few researches on how to incorporate semi-supervised learning and manifold learning,especially for face recognition.A new semi-supervised manifold learning for face recognition was proposed.This method relies on the distance matrix formed by both labeled and unlabeled samples,and then the local linear embedding (LLE) method was used to extract discriminative manifold features according to the modified distance matrix.The proposed method produces better classification performance which captures the intrinsic manifold structure collectively revealed by labeled and unlabeled samples.Experimental results on public face databases show that the proposed method can improves face classification performance effectively.