Robust ear recognition using gradient ordinal relationship pattern

Abstract

A reliable personal recognition based on ear biometrics is highly in demand due to its vast application in automated surveillance, law enforcement etc. In this paper a robust ear recognition system is proposed using gradient ordinal relationship pattern. A reference point based normalization is proposed along with a novel ear transformation over normalized ear, to obtain robust ear representations. Ear samples are enhanced using a local enhancement technique. Later a dissimilarity measure is proposed that can be used for matching ear samples. Two publicly available ear databases IITD and UND-E are used for the performance analysis. The proposed system has shown very promising results and significant improvement over the existing state of the art ear systems. The proposed system has shown robustness against small amount of illumination variations and affine transformations due to the virtue of ear transformation and tracking based matching respectively.