A Novel Robust Multispectral Palmprint Recognition Algorithm Based on P-Norm Regularization

Abstract

Since the multispectral palmprint has more spatial and frequency characteristics than the natural light condition, we can extract more feature information and obtain higher recognition accuracy. In recent years, many scholars have committed themselves to multispectral palmprint recognition studying. In this paper, a novel robust p-norm regularization model is proposed to implement the multispectral palmprint recognition. Then, a proximal iterative reweighted algorithm is employed to solve this model. Finally, we utilize the weighted fusion strategy to calculate the representation residual and carry out the recognition task efficiently. Extensive experiments on PolyU multispectral palmprint database illustrate that the proposed algorithm can achieve outstanding recognition accuracy and outperform some conventional representation methods.