DocumentCode :
3006338
Title :
Facial deblur inference to improve recognition of blurred faces
Author :
Nishiyama, Masahiro ; Takeshima, Hidenori ; Shotton, Jamie ; Kozakaya, Tatsuo ; Yamaguchi, Osamu
Author_Institution :
Corp. R&D, Toshiba Corp., Japan
fYear :
2009
fDate :
20-25 June 2009
Firstpage :
1115
Lastpage :
1122
Abstract :
This paper proposes a novel method for deblurring facial images to recognize faces degraded by blur. The main problem is how to infer a point spread function (PSF) representing the process of blur. Inferring a PSF from a single facial image is an ill-posed problem. To make this problem more tractable, our method uses learned prior information derived from a training set of blurred facial images of several individuals. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another and form a cluster. During training, we compute a statistical model of each PSF cluster in this feature space. For PSF inference we compare a query image of unknown blur with each model and select the closest one. Using the PSF corresponding to that model, the query image is deblurred, ready for recognition. Experiments on a standard face database artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared with state-of-the-art methods. We also demonstrate improved performance on real blurred images.
Keywords :
face recognition; image restoration; optical transfer function; pattern clustering; statistical analysis; PSF cluster; PSF inference; blurred face recognition; facial deblur inference; facial image deblurring; point spread function; query image; statistical model; Deconvolution; Degradation; Face recognition; Focusing; Frequency; Image recognition; Image sampling; Lighting; Space technology; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on
Conference_Location :
Miami, FL
ISSN :
1063-6919
Print_ISBN :
978-1-4244-3992-8
Type :
conf
DOI :
10.1109/CVPR.2009.5206750
Filename :
5206750
Link To Document :
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