Sparse representation-based classification (SRC) has become a popular methodology in face recognition in recent years. One widely used manner is to enforce minimum l1-norm on coding coefficient vector, which requires high computational cost. On the other hand, supervised sparse representation-based method (SSR) realizes sparse representation classification with higher efficiency by representing a probe using multiple phases. Nevertheless, since previous SSR methods only deal with Gaussian noise, they cannot satisfy empirical robust face recognition application. In this paper, we propose a robust supervised sparse representation (RSSR) model, which uses a two-phase scheme of robust representation to compute a sparse coding vector. To solve the model of RSSR efficiently, an algorithm based on iterative reweighting is proposed. We compare the RSSR with other state-of-the-art methods and the experimental results demonstrate that RSSR obtains competitive performance.
CITATION STYLE
Mi, J. X., Sun, Y., & Lu, J. (2018). Robust Face Recognition Based on Supervised Sparse Representation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10956 LNAI, pp. 253–259). Springer Verlag. https://doi.org/10.1007/978-3-319-95957-3_28
Mendeley helps you to discover research relevant for your work.