Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Facial Deblur Inference using Subspace Analysis for Recognition of Blurred Faces

Masashi Nishiyama, Abdenour Hadid, Hidenori Takeshima, Jamie Shotton, Tatsuo Kozakaya, and Osamu Yamaguchi

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.

Details

Publication typeArticle
Published inTransactions on Pattern Analysis and Machine Intelligence (TPAMI)
PublisherIEEE
> Publications > Facial Deblur Inference using Subspace Analysis for Recognition of Blurred Faces