  While existing face recognition systems based on local features are robust to
issues such as misalignment, they can exhibit accuracy degradation when
comparing images of differing resolutions. This is common in surveillance
environments where a gallery of high resolution mugshots is compared to low
resolution CCTV probe images, or where the size of a given image is not a
reliable indicator of the underlying resolution (eg. poor optics). To alleviate
this degradation, we propose a compensation framework which dynamically chooses
the most appropriate face recognition system for a given pair of image
resolutions. This framework applies a novel resolution detection method which
does not rely on the size of the input images, but instead exploits the
sensitivity of local features to resolution using a probabilistic multi-region
histogram approach. Experiments on a resolution-modified version of the
"Labeled Faces in the Wild" dataset show that the proposed resolution detector
frontend obtains a 99% average accuracy in selecting the most appropriate face
recognition system, resulting in higher overall face discrimination accuracy
(across several resolutions) compared to the individual baseline face
recognition systems.
