Original Paper

Machine Vision and Applications

, Volume 23, Issue 5, pp 985-997

First online:

Feature extraction based on fuzzy class mean embedding (FCME) with its application to face and palm biometrics

  • Minghua WanAffiliated withSchool of Information Engineering, Nanchang Hangkong UniversityKey Laboratory of Nondestructive Testing, Nanchang Hangkong University, Ministry of EducationSchool of Computer Science and Technology, Nanjing University of Science and Technology Email author 
  • , Ming LiAffiliated withSchool of Information Engineering, Nanchang Hangkong UniversityKey Laboratory of Nondestructive Testing, Nanchang Hangkong University, Ministry of Education
  • , Zhihui LaiAffiliated withSchool of Computer Science and Technology, Nanjing University of Science and Technology
  • , Jun YinAffiliated withSchool of Computer Science and Technology, Nanjing University of Science and Technology
  • , Zhong JinAffiliated withSchool of Computer Science and Technology, Nanjing University of Science and Technology

Abstract

In the local discriminant embedding (LDE) framework, the neighbor and class of data points were used to construct the graph embedding for classification problems. From a high-dimensional to a low-dimensional subspace, data points of the same class maintain their intrinsic neighbor relations, whereas neighboring data points of different classes no longer stick to one another. However, face images are always affected by variations in illumination conditions and different facial expressions in the real world. So, distant data points are not deemphasized efficiently by LDE and it may degrade the performance of classification. In order to solve above problems, in this paper, we investigate the fuzzy set theory and class mean of LDE, called fuzzy class mean embedding (FCME), using the fuzzy k-nearest neighbor (FKNN) and the class sample average to enhance its discriminant power in their mapping into a low dimensional space. In the proposed method, a membership degree matrix is firstly calculated using FKNN, then the membership degree and class mean are incorporated into the definition of the Laplacian scatter matrix. The optimal projections of FCME can be obtained by solving a generalized eigenfunction. Experimental results on the Wine dataset, ORL, Yale, AR, FERET face database and PolyU palmprint database show the effectiveness of the proposed method.

Keywords

Local discriminant embedding (LDE) Fuzzy k-nearest neighbor (FKNN) Intrinsic neighbor relations Graph embedding