Abstract
In this paper, we propose a new feature fusion approach based on local binary pattern (LBP) and sparse representation (SR). Firstly, local features are extracted by LBP and global features are sparse coefficients which are obtained via decomposing samples based on the over-complete dictionary. Then the global and local features are fused in a serial fashion. Afterwards PCA is used to reduce the dimensionality of the fused vector. Finally, SVM is employed as a classifier on the reduced feature space for classification. Experimental results obtained on publicly available databases show that the proposed feature fusion method is more effective than other methods like LBP+PCA, Gabor+PCA and Gabor+SR in terms of recognition accuracy.
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Yin, HF., Wu, XJ. (2013). A New Feature Fusion Approach Based on LBP and Sparse Representation and Its Application to Face Recognition. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_32
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DOI: https://doi.org/10.1007/978-3-642-38067-9_32
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