Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Linear Regression Correlation Filter: An Application to Face Recognition

  • Conference paper
  • First Online:
Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1022))

  • 786 Accesses

Abstract

This paper proposes a novel method of designing a correlation filter for frequency domain pattern recognition. The proposed correlation filter is designed with linear regression technique and termed as linear regression correlation filter. The design methodology of linear regression correlation filter is completely different from standard correlation filter design techniques. The proposed linear regression correlation filter is estimated or predicted from a linear subspace of weak classifiers. The proposed filter is evaluated on standard benchmark database and promising results are reported.

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

Access this chapter

Log in via an institution

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Notes

  1. 1.

    thr: hard threshold selected empirically.

  2. 2.

    http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html.

References

  1. Banerjee, P.K., Datta, A.K.: Generalized regression neural network trained preprocessing of frequency domain correlation filter for improved face recognition and its optical implementation. Opt. Laser Technol. 45, 217–227 (2013)

    Article  Google Scholar 

  2. Banerjee, P.K., Datta, A.K.: A preferential digital-optical correlator optimized by particle swarm technique for multi-class face recognition. Opt. Laser Technol. 50, 33–42 (2013)

    Article  Google Scholar 

  3. Banerjee, P.K., Datta, A.K.: Class specific subspace dependent nonlinear correlation filtering for illumination tolerant face recognition. Pattern Recognition Letters 36, 177–185 (2014)

    Article  Google Scholar 

  4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  5. Trevor, H., Robert, T., Jerome, F.: The Elements of Statistical Learning. Springer, Berlin (2009)

    MATH  Google Scholar 

  6. Jeong, K., Liu, W., Han, S., Hasanbelliu, E., Principe, J.: The correntropy mace filter. Pattern Recognit. 42(9), 871–885 (2009)

    Article  Google Scholar 

  7. Johnson, O.C., Edens, W., Lu, T.T., Chao, T.H.: Optimization of OT-MACH filter generation for target recognition. In: Proceedings of the SPIE 7340, Optical Pattern Recognition, vol. 7340, pp. 734008–734009 (2009)

    Google Scholar 

  8. Kumar, B., Savvides, M., Xie, C., Venkataramani, K., Thornton, J., Mahalanobis, A.: Biometric verification with correlation filters. Appl. Opt. 43(2), 391–402 (2004)

    Article  Google Scholar 

  9. Lai, H., Ramanathan, V., Wechsler, H.: Reliable face recognition using adaptive and robust correlation filters. Comput. Vis. Image Underst. 111, 329–350 (2008)

    Article  Google Scholar 

  10. Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)

    Article  Google Scholar 

  11. Maddah, M., Mozaffari, S.: Face verification using local binary pattern-unconstrained minimum average correlation energy correlation filters. J. Opt. Soc. Am. A 29(8), 1717–1721 (2012)

    Article  Google Scholar 

  12. Mahalanobis, A., Kumar, B., Song, S., Sims, S., Epperson, J.: Unconstrained correlation filter. Appl. Opt. 33, 3751–3759 (1994)

    Article  Google Scholar 

  13. Levine, M., Yu, Y.: Face recognition subject to variations in facial expression, illumination and pose using correlation filters. Comput. Vis. Image Underst. 104(1), 1–15 (2006)

    Article  Google Scholar 

  14. Alam, M.S., Bhuiyan, S.: Trends in correlation-based pattern recognition and tracking in forward-looking infrared imagery. Sensors (Basel, Switzerland) 14(8), 13437–13475 (2014)

    Article  Google Scholar 

  15. Imran, N., Roberto, T., Mohammed, B.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)

    Article  Google Scholar 

  16. Refregier, Ph: Filter design for optical pattern recognition: multi-criteria optimization approach. Opt. Lett. 15, 854–856 (1990)

    Article  Google Scholar 

  17. Rodriguez, A., Boddeti, V., Kumar, B., Mahalanobis, A.: Maximum margin correlation filter: a new approach for localization and classification. IEEE Trans. Image Process. 22(2), 631–643 (2013)

    Article  MathSciNet  Google Scholar 

  18. Seber, G.: Linear Regression Analysis. Wiley-Interscience (2003)

    Google Scholar 

  19. Yan, Y., Zhang, Y.: 1D correlation filter based class-dependence feature analysis forface recognition. Pattern Recognit. 41, 3834–3841 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Future Institute of Engineering and Management, Kolkata, India

    Tiash Ghosh & Pradipta K. Banerjee

Authors
  1. Tiash Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradipta K. Banerjee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tiash Ghosh .

Editor information

Editors and Affiliations

  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India

    Bidyut B. Chaudhuri

  2. Department of Advanced Information Technology and Computer Sciences, Tokyo Institute of Agriculture and Technology, Koganei, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, T., Banerjee, P.K. (2020). Linear Regression Correlation Filter: An Application to Face Recognition. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1022. Springer, Singapore. https://doi.org/10.1007/978-981-32-9088-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-32-9088-4_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9087-7

  • Online ISBN: 978-981-32-9088-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation