Face Recognition Using Null Space-Based Local Discriminant Embedding

Abstract

The manifold learning methods can discover the varying intrinsic features in face image space. However, in order to efficiently solve face image recognition problem with an image database, the extraction of discriminative features should be firstly considered. This paper proposes a new discriminative manifold learning method for face recognition. Besides like the recently proposed local perserving projectioin and local discriminative embedding algorithms which can preserve the local structure similarity in the face submanifold, our method emphasizes the discriminative property of embedding much more by a proposed Fisher Manifold Discriminant Embedding (Fisher MDE) criterion to build an object function and achieve the maximum. Experimental results on three open face datasets indicate the proposed method achieves lower error rates and provides a promising performance.