An Ensemble Learning Method Based on Random Subspace Sampling for Palmprint Identification

Abstract

Palmprint recognition is an important and widely used biometric modality with high reliability, stability and user acceptability. In this paper we propose a simple and effective ensemble learning method for palmprint identification based on Random Subspace Sampling (RSS). To achieve it, we rely on 2D-PCA to build the random subspaces. As 2D-PCA is an unsurpevised technique, features are extracted in each subspace using 2D-LDA. A simple 1-Nearest Neighbor classifier is associated to each subspace, the final decision rule being obtained by majority voting rule. The experimental results on multispectral and PolyU palmprint datasets show very encouraging performances compared to state-of-the-art techniques.