A multiple classifiers-based approach to palmvein identification

The usual trend for the conventional palmvein recognition techniques is first to extract discriminative hand-crafted feature representations from the raw images, and then feed a classifier with them. Unfortunately, it is not yet clear how the effectiveness of such features may be held in case of a large user population or in environments where the variability among acquisitions of the same person may increase. In order to face with this problem, it may be considered that the use of multiple classifiers may increase the recognition performance with respect to that of the best individual classifier, and also may handle the problem of an effective feature extraction step. In this paper, we explore the ensemble classifier approach based on Random Subspace Method (RSM), where the basic feature space is derived after a preliminary feature reduction step on the source image, and compare results achieved with and without the use of hand-crafted features. Experimental results allow us concluding that this approach leads to better results under different environmental conditions.