Abstract
Face recognition is one of the major challenging problems in image processing. To effectively deal with this issue, a novel Shearlet feature manifold method for face recognition is introduced in this paper. Specially, the Shearlet feature is first extracted to capture the geometry and edge structures of face image; then the obtained high-dimensional Shearlet features are projected into low-dimensional subspace by using local geometry analysis which can simultaneously consider intraclass geometry and interclass discrimination information; finally, the face recognition is realized in the feature space by using the nearest-neighbor classifier. The experimental results on two face datasets show the effectiveness of this proposed algorithm.
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Acknowledgements
Sincerely thank the Project supported by National key research and development project (no: 2017YFD0401004), the Special Fund for Grain scientific, Research in the Public Interest (Grant no: 201413003), and Key scientific research project of Henan Province (Grant no: 280090), and Doctoral Fund of Henan University of Technology (Grant nos: 2017BS034, 150575), and Open fund of key Laboratory of Grain Information Processing and Control (under Grant nos. KFJJ-2015-103, KFJJ-2016-103).
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Wu, J., Sun, X. & Wang, Z. Shearlet feature manifold for face recognition. J Ambient Intell Human Comput 10, 3453–3460 (2019). https://doi.org/10.1007/s12652-018-1063-1
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DOI: https://doi.org/10.1007/s12652-018-1063-1