Fusion of color spaces for Ear Authentication

In this work, we propose a local approach for 2D ear authentication based on an ensemble of matchers trained on different color spaces. This is the first work that proposes to exploit the powerful properties of color analysis for improving the performance of an ear matcher. The method described is based on the selection of color spaces from which a set of Gabor features are extracted. The selection is performed using the Sequential Forward Floating Selection where the fitness function is related to the optimization of the ear recognition performance. Finally, the matching step is performed by means of the combination by the sum rule of several 1-nearest neighbor classifiers constructed on different color components. The effectiveness of the proposed method is demonstrated using the Notre-Dame EAR dataset. Particularly interesting are the results obtained by the new approach in terms of rank-1 (~84%), rank-5 (~93%) and area under the ROC curve (~98.5%), which are better than those obtained by other state-of-the-art 2D ear matchers.