Single image deblurring for a real-time face recognition system

Blur due to motion and atmospheric turbulence is a variable that impacts the accuracy of computer vision-based face recognition techniques. However, in images captured in the wild, such variables can hardly be avoided, requiring methods to account for these degradations in order to achieve accurate results in real time. One such method is to estimate the blur and then use deconvolution to negate or, at the very least, mitigate the effects of blur. In this paper, we describe a method for estimating motion blur and a method for estimating atmospheric blur. Unlike previous blur estimation methods, both methods are fully automated and require no input parameters, thus allowing integration into a real-time facial recognition pipeline. We show experimentally, on datasets processed to include synthetic and real motion and atmospheric blur, that these techniques improve recognition more than prior work. At multiple levels of blur, our results demonstrate significant improvement over related works and our baseline on data derived from both the FERET (fairly constrained data) and Labeled Faces in the Wild (fairly unconstrained data) sets.