Skip to main content
SpringerLink
Log in

Research on two-dimensional lda for face recognition

  • Published:
Journal of Electronics (China)

Abstract

The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear discriminant analysis method makes full use of not only the row and the column direction information of face images but also the discriminant information among different classes. The method is evaluated using the Nanjing University of Science and Technology (NUST) 603 face database and the Aleix Martinez and Robert Benavente (AR) face database. Experimental results show that the method in the letter is feasible and effective.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Rama Chellappa, Charles L. Wilson, Saad Sirohey. Human and machine recognition of faces: A survey. Proceedings of IEEE, 83(1995)5, 705–740.

    Article  Google Scholar 

  2. Lu Juwei, N. Plataniotis Konstantinos, N. Venetsanopoulos Anastasios. Face recognition using kernel direct discriminant analysis algorithms. IEEE Trans. on Neural Networks, 14(2003)1, 117–126.

    Article  Google Scholar 

  3. Javier Ruiz-del-Solar, Pablo Navarrete. Eigen-space-based face recognition: A comparative study of different approaches. IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, 35(2005)3, 315–325.

    Article  Google Scholar 

  4. Yang Jian, Yang Jingyu. From image vector to matrix: A straightforward image projection technique-IMPCA vs. PCA. Pattern Recognition, 35(2002)9, 1997–1999.

    Article  MATH  Google Scholar 

  5. Yang Jian, Zhang David, F. Frangi Alejandro, Yang Jingyu. Two-dimensional PCA: A new approach to appearance-based face representation and recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(2004)1, 131–137.

    Article  Google Scholar 

  6. Li Ming, Yuan Baozong. 2D-LDA: A statistical linear discriminant analysis for image matrix. Pattern Recognition Letters, 26(2005)5, 527–532.

    Article  Google Scholar 

  7. Yang Jian, Zhang David, Yong Xu, Yang Jingyu. Two-dimensional discriminant transform for face recognition. Pattern Recognition, 38(2005)7, 1125–1129.

    Article  MATH  Google Scholar 

  8. Zhang Daoqiang, Zhou Zhihua. (2D)2PCA: Two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing, 69(2005)1–3, 224–231.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Telecom. and Info. Eng., Nanjing University of Posts and Telecom., Nanjing, 210003, China

    Han Ke Ph.D. (male, candidate) & Zhu Xiuchang

Authors
  1. Han Ke Ph.D.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhu Xiuchang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Han Ke Ph.D..

About this article

Cite this article

Han, K., Zhu, X. Research on two-dimensional lda for face recognition. J. of Electron.(China) 23, 943–947 (2006). https://doi.org/10.1007/s11767-006-0056-y

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11767-006-0056-y

Key words

Search

Navigation