Palmprint Identification based on Directional Representation

In this paper, we propose a novel approach for palmprint identification, which contains two interesting components. Firstly, we propose the directional representation for appearance based approaches. The new representation is robust to drastic illumination changes and preserves important discriminative information for classification. We then generate virtual samples to enlarge the training set to compensate for matching errors caused by large rotations and translations. Based on these two strategies, the recognition performance of representative appearance based approaches can be improved significantly. Secondly, in order to improve the robustness of palmprint identification, we propose a fusion method combining proposed method and orientation based approaches e.g. Competitive Code, which can obtain very low Equal Error Rates.