title: Eigenface-domain super-resolution for face recognition.
abstract: Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that is later passed to a face recognition system. Considering that most state-of-the-art face recognition systems use an initial dimensionality reduction method, we propose to transfer the super-resolution reconstruction from pixel domain to a lower dimensional face space. Such an approach has the advantage of a significant decrease in the computational complexity of the super-resolution reconstruction. The reconstruction algorithm no longer tries to obtain a visually improved high-quality image, but instead constructs the information required by the recognition system directly in the low dimensional domain without any unnecessary overhead. In addition, we show that face-space super-resolution is more robust to registration errors and noise than pixel-domain super-resolution because of the addition of model-based constraints.
authors.affiliation: Sch. of Electr. and Comput. Eng., Georgia Inst. of Technol., Atlanta, GA 30332-0250, USA. bahadir@ece.gatech.edu
authors.forename: Bahadir K
authors.initials: BK
authors.lastname: Gunturk
authors.forename: Aziz U
authors.initials: AU
authors.lastname: Batur
authors.forename: Yucel
authors.initials: Y
authors.lastname: Altunbasak
authors.forename: Monson H
authors.initials: MH
authors.lastname: Hayes
authors.forename: Russell M
authors.initials: RM
authors.lastname: Mersereau