Face recognition using neighborhood preserving discriminant embedding

Neighborhood preserving embedding (NPE) is a subspace learning algorithm, which has the property of preserving local neighborhood structure on the data manifold. Although NPE has been applied in many fields, it has limitations to solve recognition task. To improve the recognition performance of NPE, a new method, called neighborhood preserving discriminant embedding (NPDE), is proposed for face recognition. NPDE effectively combines the ideas of LDA and NPE, i.e. it can hold the strong discriminating power while preserving the intrinsic geometry relations of the local neighborhoods according to prior class-label information. Experimental results on ORL face database and Yale face database demonstrate the effectiveness of the proposed method.