Vein Recognition Algorithm Based on Transfer Nonnegative Matrix Factorization

Most of the existing vein recognition algorithms are only effective for specified datasets, and once replacing the vein image acquisition device, i.e., the properties of the collected vein images are changed, the performance of the algorithm will be degraded greatly. Therefore, a transfer Nonnegative Matrix Factorization (NMF) based vein recognition algorithm is proposed, which makes vein features more universal. Its contributions are mainly reflected in the following two aspects: 1) The orthogonal constraint is imposed on the model to reduce the redundancy between feature bases and increase the difference between the features of different veins; 2) The differences between the vein features in different datasets are reduced based on Maximum Mean Difference (MMD) constraint, i.e., the knowledge of the source dataset is transferred to the target dataset well, and the universality of vein features can be improved. Experimental results show that the proposed algorithm outperforms state of the art methods on two dorsal hand vein datasets and two finger vein datasets.