Improving Robustness of Single-view-based Recognition of Ears Rotated in Depth

Abstract

An algorithm that improves the robustness of an ear biometric system is proposed and targeted for use in surveillance systems based on ear biometrics. To deal with pose variation when ears are rotated in depth, Gabor jets of different poses are estimated and used as training data for a discriminant-analysis-based classifier. Experimental evaluations show the effectiveness of the proposed algorithm and the possibility of improving the robustness of a single-view-based ear surveillance-system.