Facial Deblur Inference Using Subspace Analysis for Recognition of Blurred Faces

— This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model, and thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance com...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Masashi Nishiyama, Abdenour Hadid, Hidenori Takeshima, Jamie Shotton, Tatsuo Kozakaya, Osamu Yamaguchi
• Keywords: Facial Image, Feature Space, Image, Operations Research, PAMI 2011