Robust ear identification using sparse representation of local texture descriptors

Automated personal identification using localized ear images has wide range of civilian and law-enforcement applications. This paper investigates a new approach for more accurate ear recognition and verification problem using the sparse representation of local gray-level orientations. We exploit the computational simplicity of localized Radon transform for the robust ear shape representation and also investigate the effectiveness of local curvature encoding using Hessian based feature representation. The ear representation problem is modeled as the sparse coding solution based on multi-orientation Radon transform dictionary whose solution is computed using the convex optimization approach. We also study the nonnegative formulation such problem, to address the limitations from the regularized optimization problem, in the sparse representation of localized ear features. The log-Gabor filter based approach and the localized Radon transform based feature representation has been used as baseline algorithm to ascertain the effectiveness of the proposed approach. We present experimental results from publically available UND and IITD ear databases which achieve significant improvement in the performance, both for the recognition and authentication problem, and confirm the usefulness of proposed approach for more accurate ear identification.

► A new approach for ear identification using sparse representation of local texture Information.
► A computationally simpler/effective alternative using Local Radon transform based dictionary.
► A new approach for ear identification using Hessian based encoding of local ear curvature.
► Comparative experimental results to illustrate significant performance improvement.