Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
This work addresses a specific issue in face recognition for surveillance applications, offering an incremental improvement by enhancing existing local feature-based systems.
The paper tackles the problem of accuracy degradation in face recognition systems when comparing images of differing resolutions, common in surveillance scenarios, by proposing a compensation framework that dynamically selects the most appropriate system based on a novel resolution detection method, achieving 99% accuracy in selection and higher overall discrimination accuracy across resolutions.
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.