Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference

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 (e.g. 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...

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Yongkang Wong, Conrad Sanderson, Sandra Mau, Brian Carrington Lovell
• Keywords: Appropriate Face Recognition, Computer Vision, Face Recognition, Face Recognition Systems, ICPR 2010