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

  • Huimin ZhangEmail author
  • Yan Sun
  • Haiwei Sun
  • Xin Yuan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 268)


Once the sparse representation-based classifier (SRC) was raised, it achieved a more outstanding performance than typical classification algorithm. Normally, SRC algorithm adopts \(l_1\)-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.


Compressed sensing Face recognition Feature extraction Sparse representation classification Image reconstruction 


  1. 1.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Fowler, J.E., Mun, S., Tramel, E.W.: Multiscale block compressed sensing with smoothed projected landweber reconstruction. In: Signal Processing Conference, Barcelona, pp. 564–568 (2011)Google Scholar
  3. 3.
    Liu, Y., Zhang, C., Kim, J.: Disparity-compensated total-variation minimization for compressed-sensed multiview image reconstruction. In: IEEE International Conference on Acoustics, Speech and Signal Processing, South Brisbane, pp. 1458–1462 (2015)Google Scholar
  4. 4.
    Mun, S., Fowler, J.E.: Block compressed sensing of images using directional transforms. In: IEEE International Conference on Image Processing, p. 547. IEEE (2010)Google Scholar
  5. 5.
    Tramel, E.W., Fowler, J.E.: Video compressed sensing with multihypothesis. In: Snowbird: Data Compression Conference, pp. 193–202. IEEE Computer Society (2011)Google Scholar
  6. 6.
    Zhang, J., Zhao, D., Gao, W.: Group-based sparse representation for image restoration. IEEE Trans. Image Process. 23(8), 3336–3351 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, J., Lu, C., Wang, M., et al.: Robust face recognition via adaptive sparse representation. IEEE Trans. Cybern. 44(12), 2368–2378 (2014)MathSciNetCrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA
  2. 2.Shanghai Jiao Tong UniversityShanghaiChina
  3. 3.Jiangsu UniversityZhenjiangChina

Personalised recommendations