Abstract
In biometric recognition tasks, dimensionality reduction is an important pre-process which might influence the effectiveness and efficiency of subsequent procedure. Many manifold learning algorithms arise to preserve the optimal data structure by learning a projective maps and achieve great success in biometric tasks like face recognition. In this paper, we proposed a new supervised manifold learning dimensionality reduction algorithm named Category Guided Sparse Preserving Projection (CG-SPP) which combines the global category information with the merits of sparse representation and Locality Preserving Projection (LPP). Besides the sparse graph Laplacian which preserves the intrinsic data structure of samples, a category guided graph is introduced to assist in better preserving the intrinsic data structure of subjects. We apply it to face recognition and gait recognition tasks in several datasets, namely Yale, FERET, ORL, AR and OA-ISIR-A. The experimental results show its power in dimensionality reduction in comparison with the state-of-the-art algorithms.
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Acknowledgements
The work described in this paper was partially supported by the National Natural Science Foundation of China (Grant no. 61173131, 91118005, 11202249), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1196) and the Fundamental Research Funds for the Central Universities (Grant No. CDJZR12098801, CDJRC091101 and CDJZR11095501).
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Huang, Q., Wu, Y., Zhao, C., Zhang, X., Yang, D. (2016). Category Guided Sparse Preserving Projection for Biometric Data Dimensionality Reduction. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_59
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DOI: https://doi.org/10.1007/978-3-319-46654-5_59
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