Skip to main content

Bayesian Face Recognition Approach Based on Feature Fusion

  • Conference paper
  • First Online:
Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Abstract

Feature extraction and matching recognition are two critical stages in face recognition process. While traditional Bayesian classifier exists the small sample problem in the matching recognition stage, a novel Bayesian face recognition approach based on feature fusion is proposed. In the feature extraction stage, the global non-linear feature is extracted by kernel principal component analysis (KPCA), and the local manifold structure information is extracted by the orthogonal locality sensitive discriminant analysis (OLSDA), achieving the purpose of extracting the low-dimension essential facial feature with high-discrimination, and the constraints of the fusion features make the obtained matrix be closer to the desired solution. In the matching recognition stage, a maximum entropy covariance selection (MECS) method is utilized to solve the small sample problem. Extensive experimental results on several datasets show that these two stages can significantly improve the accuracy of face recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhou, X., Sánchez, S.A., Kuijper, A.: 3D face recognition with local binary patterns. In: 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (2010)

    Google Scholar 

  2. Lai, Z., Xu, Y., Chen, Q., Yang, J., Zhang, D.: Multilinear sparse principal component analysis. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1942–1950 (2014)

    Article  Google Scholar 

  3. Triggs, N.D.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision (2005)

    Google Scholar 

  4. Saika, S., Takahashi, S., Takeuchi, M., Katto, J.: Accuracy improvement in human detection using HOG features on train-mounted camera. In: 2016 IEEE Global Conference on Consumer Electronics (2016)

    Google Scholar 

  5. Felzenszwalb, P.F., Mcallester, D.A., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2008)

    Google Scholar 

  6. Lee, J.M., Yoo, C.K., Choi, S.W., Vanrolleghem, P.A., Lee, I.B.: Nonlinear process monitoring using kernel principal component analysis. Chem. Eng. Sci. 59(1), 223–234 (2004)

    Article  Google Scholar 

  7. Hou, C., Nie, F., Yi, D., Tao, D.: Discriminative embedded clustering: a framework for grouping high-dimensional data. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1287–1299 (2017)

    MathSciNet  Google Scholar 

  8. Carli, F.P., Ferrante, A., Pavon, M., Picci, G.: A maximum entropy solution of the covariance extension problem for reciprocal processes. IEEE Trans. Autom. Control 56(9), 1999–2012 (2011)

    Article  MathSciNet  Google Scholar 

  9. Kim, K.I., Franz, M.O., Scholkopf, B.: Iterative kernel principal component analysis for image modeling. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1351–1366 (2005)

    Article  Google Scholar 

  10. Yi, J., Ruan, Q.: Orthogonal locality sensitive discriminant analysis for face recognition. In: 2014 International Conference on Signal Processing (2014)

    Google Scholar 

  11. Driessen, B., Dürmuth, M.: Achieving anonymity against major face recognition algorithms. In: 2013 IFIP International Conference on Communications and Multimedia Security (2013)

    Google Scholar 

  12. Molaei, A.M., Ebrahimzadeh, A.: Optimal steganography with blind detection based on Bayesian optimization algorithm. Pattern Anal. Appl. 22(1), 205–219 (2019)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Natural Science Foundation of China with Grant Nos. (61525401, 61234002, 51705304), Natural Science Foundation of Shanghai with Grant Nos. (19ZR1420800, 16ZR1413400), the Program of Shanghai Academic/Technology Research Leader with Grant No. 16XD1400300, the Major Scientific and Technological Innovation Projects of Shanghai Education Commission with Grant No. 2017-01-07-00-07-E00026.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xiaoyang Zeng or Mingyu Wang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, J. et al. (2020). Bayesian Face Recognition Approach Based on Feature Fusion. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_4

Download citation

Publish with us

Policies and ethics