Weighted Linear Embedding and Its Applications to Finger-Knuckle-Print and Palmprint Recognition

Abstract

In this paper we propose a new linear feature extraction approach called Weighted Linear Embedding (WLE). WLE combines Fisher criterion with manifold learning criterion like local discriminant embedding analysis (LDE), whereas unlike LDE that only utilizes local neighbor information it uses local information and nonlocal information simultaneously. WLE is also unlike linear discriminant analysis (LDA) that treats local information and nonlocal information equally, and it uses these two kinds of information discriminatively by utilizing the Gaussian weighting. Hence, WLE is more powerful than LDA and LDE for feature extraction. Experimental results on the PolyU finger-knuckle-print database and the PolyU palmprint database indicate that our WLE algorithm outperforms principal components analysis (PCA), LDA and LDE.