Research of Face Recognition Based on Locally Linear Embedding
Cuihong Zhou, Gelan Yang · 2009
Image data taken with various capturing devices are usually multidimensional and therefore they are not very suitable for accurate classification normally expecting to operate only on a small set of relevant features. Locally linear embedding is an effective nonlinear dimensionality reduction method for exploring the intrinsic characteristics of high dimensional data. In this paper, novel local linear embedding for face classification is proposed. We modify the LLE algorithm by preserving more geometrical knowledge of the high-dimensional data, then combining with simple classifiers such as the nearest mean classifier. Experimental simulations are shown to yield remarkably good classification results in high dimension face image sequence.