Heterogeneous face recognition via Grassmannian based nearest subspace search

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Author(s)
Tian, Yuan
Yan, Cheng
Bai, Xiao
Zhou, Jun
Griffith University Author(s)
Year published
2017
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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) ...
View more >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.
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View more >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.
View less >
Conference Title
2017 24TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
Volume
2017-September
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Subject
Image processing