Regularized Shearlet Network for face recognition using single sample per person

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach includes a module based on regularization theory (RSN) to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address 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, such as AR, FERET, FRGC, FEI, CK. Our tests show that the RSN approach is very competitive and outperforms several standard face recognition methods.