Fusion of locally linear embedding and principal component analysis for face recognition (FLLEPCA)

We proposed a novel approach for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) and Principal Component Analysis (PCA). LLE computes a compact representation of high dimensional data combining the major advantages of linear methods, With the advantages of nonlinear approaches which is flexible to learn a broad of class on nonlinear manifolds. The application of LLE, however, is limited due to its lack of a parametric mapping between the observation and the low-dimensional output. In addition, the revealed underlying manifold can only be observed subjectively. To overcome these limitations, we propose our method for recognition by fusion of LLE and Principal Component Analysis (FLLEPCA) and validate their efficiency. Experiments on CMU AMP Face Expression Database and JAFFE databases show the advantages of our proposed novel approach.