A Novel Index-Based Rank Fusion Method for Occluded Ear Recognition

Ear biometrics are often partially or fully occluded by hair, earrings, headphones, hat/cap, scarf, and other obstacles. Occurrence of occlusion during identification stage may cause significant information loss, which deteriorates recognition performance. In this paper, we proposed a novel index-based rank fusion method for ear recognition that can utilize occlusion information adaptively during identification stage to decide on a person´s identity. In the proposed method, feature sets are selected and weighted according to the proportion of occlusion during identification time. Our experimental results on wide variety of real as well as synthetically occluded ears demonstrate that the proposed adaptive feature selection and fusion method significantly improves the recognition performance of occluded ears.