Adaptive Two Phase Sparse Representation Classifier for Face Recognition
Sparse Representation Classifier proved to be a powerful classifier that is more and more used by computer vision and signal processing communities. On the other hand, it is very computationally expensive since it is based on an L 1 minimization. Thus, it is not useful for scenarios demanding a rapid decision or classification. For this reason, researchers have addressed other coding schemes that can make the whole classifier very efficient without scarifying the accuracy of the original proposed SRC. Recently, two-phase coding schemes based on classic Regularized Least Square were proposed. These two-phase strategies can use different schemes for selecting the examples that should be handed over to the next coding phase. However, all of them use a fixed and predefined number for these selected examples making the performance of the final classifier very dependent on this ad-hoc choice. This paper introduces three strategies for adaptive size selection associated with Two Phase Test Sample Sparse Representation classifier. Experiments conducted on three face datasets show that the introduced schemes can outperform the classic two-phase strategies. Although the experiments were conducted on face datasets, the proposed schemes can be useful for a broad spectrum of pattern recognition problems.
KeywordsTraining Sample Face Recognition Recognition Rate Sparse Representation Near Neighbor
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