Generally, the commonly used sparse-based methods, such as sparse representation classifier, have achieved a good recognition result in face recognition. However, there exist several problems in those methods. First, ...
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Generally, the commonly used sparse-based methods, such as sparse representation classifier, have achieved a good recognition result in face recognition. However, there exist several problems in those methods. First, those methods think that the importance of each atom is the same in representing other query samples. This is not reasonable because different atoms contain different amounts of information, their importance should be different when they together represent the query samples. Second, those methods cannot meet the real-time requirement when dealing with large data set. In this study, on the one hand, the authors propose a fast extended sparse-weighted representation classifier (FESWRC) by considering the different importance of atoms and using primal augmented Lagrangian method as well as principal component analysis. On the other hand, the authors propose a distinctive feature descriptor, named logarithmic-weighted sum (LWS) feature descriptor. The authors combine FESWRC and LWS and used for face recognition, this method is called face recognition algorithm based on feature descriptor and weighted linear sparse representation (FDWLSR). Experimental results show that FDWLSR can realise real-time recognition and the recognition rate can achieve 100.0, 100.0, 91.6, 93.4 and 87.4%, respectively, on the Yale, Olivetti Research Laboratory (ORL), faculdade de engenharia industrial (FEI), face recognition technology program (FERET) and labelled face in the wild datasets.
Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch proble...
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Recently, many sparse-based direction-of-arrival (DOA) estimation methods for coprime arrays have become popular for their excellent detection performance. However, these methods often suffer from grid mismatch problem due to the discretization of the potential angle space, which will cause DOA estimation performance degradation when the target is off-grid. To this end, we proposed a sparse-based off-grid DOA estimation method for coprime arrays in this paper, which includes two parts: coarse estimation process and fine estimation process. In the coarse estimation process, the grid points closest to the true DOAs, named coarse DOAs, are derived by solving an optimization problem, which is constructed according to the statistical property of the vectorized covariance matrix estimation error. Meanwhile, we eliminate the unknown noise variance effectively through a linear transformation. Due to finite snapshots effect, some undesirable correlation terms between signal and noise vectors exist in the sample covariance matrix. In the fine estimation process, we therefore remove the undesirable correlation terms from the sample covariance matrix first, and then utilize a two-step iterative method to update the grid biases. Combining the coarse DOAs with the grid biases, the final DOAs can be obtained. In the end, simulation results verify the effectiveness of the proposed method.
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