Face Recognition Using KFDA-LLE

Abstract

Locally Linear Embedding (LLE) is a recently proposed algorithm for non-linear dimensionality reduction and manifold learning. However, since LLE aims to discover the intrinsical low dimensional variables from high dimensional nonlinear data, it may not be optimal for classification problem. In this paper, an improved version of LLE, namely KFDA-LLE, is proposed using kernel Fisher discriminant analysis (KFDA) method for face recognition task. First, the input training samples are projected into the low-dimensional space by LLE. Then KFDA is introduced for finding the optimal projection direction. Experimental results on face database demonstrate that the proposed method excels the other methods.