Weighted-PCANet, a novel feature learning method is proposed to face recognition by combining linear regression classification model (LRC) and PCANet construction. The sample specific hat matrix is used to handle diff...
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ISBN:
(纸本)9783319265612;9783319265605
Weighted-PCANet, a novel feature learning method is proposed to face recognition by combining linear regression classification model (LRC) and PCANet construction. The sample specific hat matrix is used to handle different images in feature extraction stage. After appropriate adaption, the performance of this new model outperform than various mainstream methods including PCANet for face recognition on Extended YaleB dataset. Particularly, various experiments testify the robustness of weighted-PCANet while dealing with less training samples or corrupted data.
In this letter, we propose a unitary regressionclassification (URC) algorithm, which could achieve total minimum projection error, to improve the robustness of face recognition. Starting from linearregression classi...
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In this letter, we propose a unitary regressionclassification (URC) algorithm, which could achieve total minimum projection error, to improve the robustness of face recognition. Starting from linear regression classification, the goal of the proposed URC method is to minimize the total within-class projection error of all classes to seek the unitary projection for face classification. In the recognition phase, the recognition is determined by calculating the minimum projection error on the unitary rotation subspace. Experimental results carried out on FEI and FERET facial databases reveal that the proposed algorithm outperforms the state-of-the-art methods in face recognition.
Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The pro...
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Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.
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