Face sequence recognition using Grassmann Distances and Grassmann Kernels

In this paper we show how Grassmann distances and Grassmann kernels can be efficiently used to learn and classify face sequence videos. We propose two new methods, the Grassmann Distance Mutual Subspace Method (GD-MSM) which uses Grassmann distances to define the similarity between subspaces of images, and the Grassmann Kernel Support Vector Machine (GK-SVM), which applies two Grassmann kernels - the projection kernel and the Binet-Cauchy kernel - in a convex optimization scheme, using the Support Vector Machine (SVM) framework. GD-MSM and GK-SVM are compared in a face recognition task with several related methods using a large database of face image sequences from 100 subjects, containing expression changes related to a natural conversation setting. Additionally, we study the effect of combining all available training image sequences into a single subspace per category, in comparison with using multiple smaller subspaces, i.e. representing each category by several different subspaces, where each subspace is formed from image sequences taken under different conditions.