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This paper presents an improved approach to face recognition, called Regularized Shear let Network (RSN), that takes advantage of the sparse representation properties of shear lets in biometric applications. The main novelty of our approach is the efficient extraction of geometric features based on the properties of the shear let decomposition, a multiscale directional method which is especially designed to capture directional and anisotropic information in multidimensional data. To further improve the performance of our face recognition algorithm, we include a regularization step to control the trade-off between the fidelity to the data (gallery) and smoothness of the solution (probe). In this work, we focus on the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-arts method using several facial databases including AR, FERET, FRGC, FEI and CK Our tests show that our RSN algorithm is very competitive and outperforms several state-of-the-art face recognition methods. |
