Face Recognition Based on Extended Locally Linear Embedding
Lei Zhu, Shuaishuai Zhu · 2006
Face image data taken with various capturing devices are usually high dimensional and not very suitable for accurate classification. In this paper, a new face recognition method based on nonlinear dimensionality reduction is proposed. The extended locally linear embedding (ELLE) first embeds the high dimensional face data into a low dimensional hidden manifold. Then the linear discriminant analysis (LDA) is performed to find an optimal projection direction for classification. The proposed method was tested and evaluated using the AT&T and Yale face databases. Recognition rates were compared with Eigenface, Fisherface and LLE. Experimental results indicated the promising performance of the proposed method