Recognition of Blurred Faces via Facial Deblurring Combined with Blur-Tolerant Descriptors

Blur is often present in real-world images and significantly affects the performance of face recognition systems. To improve the recognition of blurred faces, we propose a new approach which inherits the advantages of two recent methods. The idea consists of first reducing the amount of blur in the images via deblurring and then extracting blur-tolerant descriptors for recognition. We assess our analysis on real blurred face images (FRGC 1.0 database) and also on face images artificially degraded by focus blur (FERET database), demonstrating significant performance enhancement compared to the state-of-the-art.