1 research outputs found
Coupled Learning for Facial Deblur
Blur in facial images significantly impedes the efficiency of recognition
approaches. However, most existing blind deconvolution methods cannot generate
satisfactory results due to their dependence on strong edges, which are
sufficient in natural images but not in facial images. In this paper, we
represent point spread functions (PSFs) by the linear combination of a set of
pre-defined orthogonal PSFs, and similarly, an estimated intrinsic (EI) sharp
face image is represented by the linear combination of a set of pre-defined
orthogonal face images. In doing so, PSF and EI estimation is simplified to
discovering two sets of linear combination coefficients, which are
simultaneously found by our proposed coupled learning algorithm. To make our
method robust to different types of blurry face images, we generate several
candidate PSFs and EIs for a test image, and then, a non-blind deconvolution
method is adopted to generate more EIs by those candidate PSFs. Finally, we
deploy a blind image quality assessment metric to automatically select the
optimal EI. Thorough experiments on the facial recognition technology database,
extended Yale face database B, CMU pose, illumination, and expression (PIE)
database, and face recognition grand challenge database version 2.0 demonstrate
that the proposed approach effectively restores intrinsic sharp face images
and, consequently, improves the performance of face recognition