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Compressive sensing theory shows that sparse signals can be reconstructed from far less samples than those required by the classical Shannon-Nyquist Theorem. An optimized sensing matrix for a certain class of signals can further reduce the necessary number of samples. Additionally, in order to make the signals represented as sparse as possible, a dictionary can be optimized. In this paper, we introduce...
Face recognition is an element of many smartphone apps, e.g. face unlocking, people tagging and games. Sparse Representation Classification (SRC) is a state-of-the-art face recognition algorithm, which has been shown to outperform many classical face recognition algorithms in OpenCV. The success of SRC is due to its use of ℓ1 optimisation, which makes SRC robust to noise and occlusions. Since ℓ1 optimisation...
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