A supervised nonlinear local embedding for face recognition

Jian Cheng, Qingshan Liu, Hanqing Lu, Yen-Wei Chen

Abstract: Many recent works demonstrated that subspace analysis is a good method for face recognition. How to find the subspace is a key issue. In this paper, a supervised nonlinear local embedding (SNLE) method is proposed to construct a subspace for face recognition, in which we combine the idea of nonlinear kernel mapping and preserving local geometric relations of the samples belonging to same class. SNLE can not only gain a perfect approximation of the nonlinear face manifold, but also enhance within-class local information. Moreover, it is also equivalent to solving a generalized eigenvalue problem in mathematics. Our experiments are performed on two benchmarks, and experimental results show that the proposed method has an encouraging performance.