Abstract: The key issues of video based face recognition are the way to model facial images
precision and high efficiency and measure the similarity between two sets. To end this, a Grassmann
manifold dimension reduction method is proposed to improve the performance of image set
matching. Firstly, a subspace constructed by an image set is presented as a point in a Grassmann
manifold with a projection matrix. Then, a projection metric learning approach is applied to reduce
the dimension of the orthogonal basis matrix to obtain a lower dimension and tighten Grassmann
manifold. Finally, a kernel function mapped the orthogonal basis matrix from a Grassmann manifold
to Euclidean space for classification. Extensive experimental results on shared video based dataset
show that the proposed method is an effective object matching and face recognition method based on
set-to-set matching.

Key words: subspace, set matching, Grassmann manifold, projection metric, metric learning