Mar 14, 2016 · Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank ...

We propose a signal level fusion method based on low rank approximation. First, we pro- pose a sparse-error low rank matrix factorization (SE-LRMF) model to ...

Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank component and a ...

Specifically, we propose a sparse-error low rank matrix factorization model to perform LRA, decomposing the noisy captures into a low rank component and a ...

Signal-Level Information Fusion for Less Constrained Iris Recognition Using Sparse-Error Low Rank Matrix Factorization · Figures and Tables · Topics · 10 Citations ...

Signal-Level Information Fusion for Less Constrained Iris Recognition Using Sparse-Error Low Rank Matrix Factorization. Yang Hu, Konstantinos Sirlantzis ...

Dec 10, 2022 · This method seeks not only low level features, but also high level feature distributions for more accurate and robust iris liveness detection.

In this work, we propose a novel occlusion-robust, deformation-robust, and alignment-free framework for low-quality iris matching, which integrates the merits ...

In this paper, we developed a signal decomposition approach for the purpose of anomaly detection based on the idea of low rank and sparse decomposition taking ...

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