Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings

Research Article

The Realization of Face Recognition Algorithm Based on Compressed Sensing (Short Paper)

  • @INPROCEEDINGS{10.1007/978-3-030-12981-1_40,
        author={Huimin Zhang and Yan Sun and Haiwei Sun and Xin Yuan},
        title={The Realization of Face Recognition Algorithm Based on Compressed Sensing (Short Paper)},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 14th EAI International Conference, CollaborateCom 2018, Shanghai, China, December 1-3, 2018, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2019},
        month={2},
        keywords={Compressed sensing Face recognition Feature extraction Sparse representation classification Image reconstruction},
        doi={10.1007/978-3-030-12981-1_40}
    }
    
  • Huimin Zhang
    Yan Sun
    Haiwei Sun
    Xin Yuan
    Year: 2019
    The Realization of Face Recognition Algorithm Based on Compressed Sensing (Short Paper)
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-12981-1_40
Huimin Zhang,*, Yan Sun1,*, Haiwei Sun1,*, Xin Yuan1,*
  • 1: Jiangsu University
*Contact email: lizzyww@umich.edu, 614564957@qq.com, 329788350@qq.com, jund85@163.com

Abstract

Once the sparse representation-based classifier (SRC) was raised, it achieved a more outstanding performance than typical classification algorithm. Normally, SRC algorithm adopts -norm minimization method to solve the sparse vector, and its computation complexity increases correspondingly. In this paper, we put forward a compressed sensing reconstruction algorithm based on residuals. This algorithm utilizes the local sparsity within figures as well as the non-local similarity among figure blocks to boost the performance of the reconstruction algorithm while remaining a median computation complexity. It achieves a superior recognition rate in the experiments of Yale facial database.