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Mesh motion scale invariant feature and collaborative learning for visual recognition

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Abstract

Visual recognition has been gradually played important roles in many fields. An effective feature descriptor, with higher discrimination and higher descriptiveness for the different visual recognition tasks, is a challenging issue. In this paper, we propose a novel feature, called mesh motion scale invariant feature description, to facilitate the different visual task description and balance discrimination and efficiency. Then, a hierarchical collaborative feature learning model for multi-visual tasks in complex scenes is presented for obtaining the recognition results. Four large databases, FRGC, CASIA, BU-3DFE and 3D Online Action, are introduced to the performance comparison and the experimental results show a better performance for face recognition, expression recognition and activity recognition based on our proposed method.

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Acknowledgements

The work presented in this paper was supported by the National Natural Science Foundation of China (Grants No. NSFC-61402046), Fund for the Doctoral Program of Higher Education of China (Grants No. 20120005110002), National Great Science Specific Project (Grants No. 2011ZX0300200301, 2012ZX03005008) and Beijing Municipal Commission of Education Build Together Project.

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Authors and Affiliations

  1. Beijing Key Laboratory of Work Safety Intelligent Monitoring, School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Yue Ming

  2. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, People’s Republic of China

    Jiakun Shi

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  1. Yue Ming
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Correspondence to Yue Ming.

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Ming, Y., Shi, J. Mesh motion scale invariant feature and collaborative learning for visual recognition. Multimed Tools Appl 77, 22367–22384 (2018). https://doi.org/10.1007/s11042-018-5969-6

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