Abstract
Subspace-based holistic registration is introduced as an alternative to landmark-based face registration, which has a poor performance on low-resolution images, as obtained in camera surveillance applications. The proposed registration method finds the alignment by maximizing the similarity score between a probe and a gallery image. We use a novel probabilistic framework for both user-independent as well as user-specific face registration. The similarity is calculated using the probability that the face image is correctly aligned in a face subspace, but additionally we take the probability into account that the face is misaligned based on the residual error in the dimensions perpendicular to the face subspace. We perform extensive experiments on the FRGCv2 database to evaluate the impact that the face registration methods have on face recognition. Subspace-based holistic registration on low-resolution images can improve face recognition in comparison with landmark-based registration on high-resolution images. The performance of the tested face recognition methods after subspace-based holistic registration on a low-resolution version of the FRGC database is similar to that after manual registration.
Highlights
Face recognition in the context of camera surveillance is still a challenging problem
In the Face Recognition Vendor Test [1], low-resolution face images are defined to contain an interocular distance of 75 pixels, we used even lower resolutions with interocular distances of 50 pixels and lower
Face registration on low-resolution images is in these cases often omitted and the region found by the face detection is directly used for face recognition [2, 3]
Summary
Face recognition in the context of camera surveillance is still a challenging problem. It is crucial that an acquired facial image is registered to a reference coordinate system. Most conventional registration methods are based on landmarks. To locate these landmarks accurately, high-resolution images are needed. For those methods, it is problematic to register low resolution facial images as obtained in video surveillance. High-resolution face images have an interocular distance of more than 100 pixels. Face registration on low-resolution images is in these cases often omitted and the region found by the face detection is directly used for face recognition [2, 3]. Accurate face registration can contribute to better recognition performance on low-resolution images.
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