Feature Space Hausdorff Distance for Face Recognition
We propose a novel face image similarity measure based on Hausdorff distance (HD). In contrast to conventional HD-based measures, which are generally applied in the image space (such as edge maps or gradient images), the proposed HD-based similarity measure is applied in the feature space. By extending the concept of HD using a variable radius and reference set, we can generate a neighbourhood set for HD measures in feature space and then apply this concept for classification. Experiments on the ‘Labeled Faces in the Wild’ and FRGC datasets show that the proposed measure improves the overall classification performance quite dramatically, especially under the highly desirable low false acceptance rate conditions.