Kernel Discriminant Embedding in face recognition

In this paper, we present a novel and effective feature extraction technique for face recognition. The proposed technique incorporates a kernel trick with Graph Embedding and the Fisher’s criterion which we call it as Kernel Discriminant Embedding (KDE). The proposed technique projects the original face samples onto a low dimensional subspace such that the within-class face samples are minimized and the between-class face samples are maximized based on Fisher’s criterion. The implementation of kernel trick and Graph Embedding criterion on the proposed technique reveals the underlying structure of data. Our experimental results on face recognition using ORL, FRGC and FERET databases validate the effectiveness of KDE for face feature extraction.

► Incorporation of kernel trick, Graph Embedding and Fisher criteria in KDE.
► Kernel trick reveals underlying nonlinear data structure.
► KDE learns data from neighbourhood geometrical structure.
► The intrinsic structures implicitly exhibit certain degree of discriminating power.
► Fisher’s criterion in KDE explicitly enhances the discriminating capability.