Skip to main content
SpringerLink
Log in
Book cover

Kernel Learning Algorithms for Face Recognition pp 101–133Cite as

Kernel Discriminant Analysis Based Face Recognition

  • Chapter
  • First Online:

  • 1813 Accesses

Abstract

Linear discriminant analysis (LDA) is a traditional dimensionality reduction technique for feature extraction. It has been widely used and proven successful in a lot of real-world applications. LDA works well in some cases, but it fails to capture a nonlinear relationship with a linear mapping. In order to overcome this weakness of LDA, the kernel trick is used to represent the complicated nonlinear relationships of input data to develop kernel discriminant analysis (KDA) algorithm.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   159.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Müller KR, Mika S, Rätsch G, Tsuda K, Schölkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Networks 12:181–201

    Article  Google Scholar 

  2. Li J-B, Chu S-C, Pan J-S, Ho J-H (2007) Adaptive data-dependent matrix norm based Gaussian kernel for facial feature extraction. Int J Innovative Comput, Inf Control 3(5):1263–1272

    Google Scholar 

  3. Sahbi H (2007) Kernel PCA for similarity invariant shape recognition. Neurocomputing 70:3034–3045

    Article  Google Scholar 

  4. Mika S, Ratsch G, Weston J, Schölkopf B, Muller KR (1999) Fisher discriminant analysis with kernels. In: Proceedings of IEEE international workshop on neural networks for signal processing IX, pp 41–48, Aug 1999

    Google Scholar 

  5. Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099

    Article  Google Scholar 

  6. Schölkopf B, Smola A, Muller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  7. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Networks 14(1):117–226

    Article  Google Scholar 

  8. Baudat G, Anouar F (2000) Generalized discriminant analysis using a kernel approach. Neural Comput 12(10):2385–2404

    Article  Google Scholar 

  9. Liang Z, Shi P (2005) Uncorrelated discriminant vectors using a kernel method. Pattern Recogn 38:307–310

    Article  MATH  Google Scholar 

  10. Liang Z, Shi P (2004) Efficient algorithm for kernel discriminant anlaysis. Pattern Recogn 37(2):381–384

    Article  Google Scholar 

  11. Liang Z, Shi P (2004) An efficient and effective method to solve kernel Fisher discriminant analysis. Neurocomputing 61:485–493

    Article  Google Scholar 

  12. Yang J, Frangi AF, Yang J, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  13. Yang MH (2002) Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: Proceedings of 5th IEEE international conference on automatic face and gesture recognition, pp 215–220, May 2002

    Google Scholar 

  14. Lu J, Plataniotis KN, Venetsanopoulos AN (2003) Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans Neural Networks 14(1):117–126

    Article  Google Scholar 

  15. Zheng W, Zou C, Zhao L (2005) Weighted maximum margin discriminant analysis with kernels. Neurocomputing 67:357–362

    Article  Google Scholar 

  16. Huang J, Yuen PC, Chen WS, Lai JH (2004) Kernel subspace LDA with optimized kernel parameters on face recognition. In: Proceedings of the 6th IEEE international conference on automatic face and gesture recognition 2004

    Google Scholar 

  17. Wang L, Chan KL, Xue P (2005) A criterion for optimizing kernel parameters in KBDA for image retrieval. IEEE Trans Syst, Man Cybern-Part B: Cybern 35(3):556–562

    Article  Google Scholar 

  18. Chen WS, Yuen PC, Huang J, Dai D-Q (2005) Kernel machine-based one-parameter regularized fisher discriminant method for face recognition. IEEE Trans Syst, Man Cybern-Part B: Cybern 35(4):658–669

    Google Scholar 

  19. Liang Y, Li C, Gong W, Pan Y (2007) Uncorrelated linear discriminant analysis based on weighted pairwise Fisher criterion. Pattern Recogn 40:3606–3615

    Article  MATH  Google Scholar 

  20. Zheng Y, Yang J, Yang J, Wu X (2006) A reformative kernel Fisher discriminant algorithm and its application to face recognition. Neurocomputing 69(13-15):1806–1810

    Article  Google Scholar 

  21. Tao D, Tang X, Li X, Rui Y (2006) Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm. IEEE Trans Multimedia 8(4):716–727

    Article  Google Scholar 

  22. Xu Y, Zhang D, Jin Z, Li M, Yang J-Y (2006) A fast kernel-based nonlinear discriminant analysis for multi-class problems. Pattern Recogn 39(6):1026–1033

    Article  MATH  Google Scholar 

  23. Saadi K, Talbot NLC, Cawley GC (2007) Optimally regularised kernel Fisher discriminant classification. Neural Networks 20(7):832–841

    Article  MATH  Google Scholar 

  24. Yeung D-Y, Chang H, Dai G (2007) Learning the kernel matrix by maximizing a KFD-based class separability criterion. Pattern Recogn 40(7):2021–2028

    Article  MATH  Google Scholar 

  25. Shen LL, Bai L, Fairhurst M (2007) Gabor wavelets and general discriminant analysis for face identification and verification. Image Vis Comput 25(5):553–563

    Article  Google Scholar 

  26. Ma B, Qu H, Wong H (2007) Kernel clustering-based discriminant analysis. Pattern Recogn 40(1):324–327

    Article  MATH  Google Scholar 

  27. Wu X-H, Zhou J-J (2006) Fuzzy discriminant analysis with kernel methods. Pattern Recogn 39(11):2236–2239

    Article  MATH  Google Scholar 

  28. Liu Q, Lu H, Ma S (2004) Improving kernel Fisher discriminant analysis for face recognition. IEEE Trans Pattern Anal Mach Intell 14(1):42–49

    Google Scholar 

  29. Lanckriet G, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI (2004) Learning the kernel matrix with semidefinte programming. J Mach Learn Res 5:27–72

    MATH  Google Scholar 

  30. Xiong H, Swamy MNS, Ahmad MO (2005) Optimizing the kernel in the empirical feature space. IEEE Trans Neural Networks 16(2):460–474

    Article  Google Scholar 

  31. Peng J, Heisterkamp DR, Dai HK (2004) Adaptive quasiconformal kernel nearest neighbor classification. IEEE Trans Pattern Anal Mach Intell 26(5):656–661

    Article  Google Scholar 

  32. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Network 12(6):783–789

    Article  Google Scholar 

  33. Li Z, Lin D, Tang X (2009) Nonparametric discriminant analysis for face recognition. IEEE Trans Pattern Anal Mach Intell 31(4):755–761

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automatic Test and Control, Harbin Institute of Technology, Yi-Kuang Street 2, Harbin, People’s Republic of China

    Jun-Bao Li

  2. School of Information and Engineering, Flinders University of South Australia, Sturt Road, Bedford Park, SA, 5042, Australia

    Shu-Chuan Chu

  3. HIT Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen City, Guangdong Province, People’s Republic of China

    Jeng-Shyang Pan

Authors
  1. Jun-Bao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu-Chuan Chu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeng-Shyang Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun-Bao Li .

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Li, JB., Chu, SC., Pan, JS. (2014). Kernel Discriminant Analysis Based Face Recognition. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-0161-2_5

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-0160-5

  • Online ISBN: 978-1-4614-0161-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation