Recognition of faces using texture-based principal component analysis and Grassmannian distances analysis

Abstract

This paper introduces a new face recognition method-texture-based Principal Component Analysis (PCA), which employs PCA on texture features.Initially, the eigenspace of texture images is created by eigenvalues and eigenvectors.From this space,the eigentextures are constructed,and most of the eigentextures are selected by using PCA.With these eigentextures, we generalize Grassmannian distances into texture feature space to recognize.We address the problem of face recognition in terms of the subject-specific subspaces instead of image vectors.The proposed method is tested on Essex Face 94 database,and it has been demenstrated to have a promising performance.

The Flag Manifold as a Tool for Analyzing and Comparing Sets of Data Sets

“…Initially explored in the setting of subspace packing problems [30,5,16], the application of Stiefel and Grassmann manifolds has become widespread in computer vision and pattern recognition. Examples include: video processing arXiv:2006.14086v1 [cs.CV] 24 Jun 2020 A PREPRINT -JUNE 26, 2020 [12], classification, [11,4,33,34], action recognition [2], expression analysis [31,32,17], domain adaptation [15,28], regression [29,13], pattern recognition [18], and computation of subspace means [3,22]. More recently, Grassmannians have also been explored in the deep neural network literature [14].…”