Heterogeneous face recognition via grassmannian based nearest subspace search

Heterogeneous face recognition involves matching faces in different image modalities, such as near infrared images to visible images or sketch images to photos. This challenging task has attracted increasing attention in recent years. This paper presents, for the first time, a subspace based method to tackle the problem of face recognition between visible images (VIS) and near infrared (NIR) images. Subspace is used to extract essential attributes from VIS and NIR images. We adopt Grassmannian radial basis function (RBF) kernel to keep the relationship between subspaces, and use kernel canonical correlation analysis (KCCA) to handle correlation mapping between VIS and NIR domains. After mapping both VIS and NIR images to the common space, the heterogeneous face recognition problem can be easily completed by the nearest search. We evaluate the proposed method on the CASIA NIR-VIS 2.0 dataset. The experimental results demonstrate that our method is very effective for NIR-VIS face recognition.