Improving the Robustness of Single-View Ear-Based Recognition under a Rotated in Depth Perspective

An algorithm is proposed for improving the robustness of an ear biometric system with the aim of developing a surveillance-system based on ear biometrics. To deal with pose variations that are rotated in depth, the 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 potential for improving the robustness of a single-view-based ear surveillance-system.