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.