Palm Recognition Using Fast Sparse Coding Algorithm

A novel palmprint recognition method using the fast sparse coding (FSC) algorithm is proposed in this paper. This algorithm is based on iteratively solving two convex optimization problems, the L-1-regularized least squares problem and the L-2-constrained least squares problem. As the same as the standard sparse coding (SC) algorithm, this FSC algorithm can model the receptive fields of neurons in the visual cortex in brain of human, however, it has a faster convergence speed than the existing SC model. Using this FSC algorithm, feature basis vectors of palmprint images can be learned successfully. Here, the PolyU palmprint database, used widely in palmprint recognition research, is selected as the test database. Furthermore, utilizing learned palmprint features and the radial basis probabilistic neural network (RBPNN) classifier, the task of palmprint recognition can be implemented efficiently. Using the recognition rate as the measure criterion, and compared our palmprint recognition method with principal component analysis (PCA), standard SC and fast independent component analysis (FastICA), the simulation results show further that this method proposed by us is indeed efficient in application.