Journal of Electrical and Electronic Engineering

| Peer-Reviewed |

Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition

Received: 11 May 2013    Accepted:     Published: 10 June 2013
Views:       Downloads:

Share This Article

Abstract

The paper compares two feature extraction techniques for face recognition with Gabor Filters. Firstly Gabor Filters based methods which mainly use only Gabor magnitude features like Gabor Fisher Classifier (GFC), and secondly the proposed method called the Phase-based Gabor Fisher Classifier (PBGFC) by turk[3]. The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. In ours experiments we use the ORL data base, the feasibility of the proposed methods was assessed in a series of face verification experiments. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as (LDA), while it ensures nearly similar verification performance as the established Gabor Fisher Classifier (GFC).

DOI 10.11648/j.jeee.20130102.11
Published in Journal of Electrical and Electronic Engineering (Volume 1, Issue 2, June 2013)
Page(s) 41-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Face Recognition, Gabor Filter, Gabor Phase Congruency

References
[1] C. Liu, "Capitalize on dimensionality increasing techniques for improving face recognition grand challenge performance," IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467–476,2002.
[2] J. Short, J. Kittler, and K. Messer, "Photometric normalisation for face verification," in Proceedings of the 5th AVBPA, New York, USA, July 2005, pp. 617–626.
[3] V. ˇStruc, and N. Paveˇsi´c, The Complete Gabor-Fisher Classifier for Robust Face Recognition, EURASIP Advances in Signal Processing, vol. 2010, 26 pages, doi:10.1155/2010/847680, 2010.
[4] C. Liu and H. Wechsler, "Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition," IEEE
[5] L. Shen, L. Bai, and M. Fairhurst, "Gabor wavelets and general discriminant analysis for face identification and verification," Image and Vision Computing, vol. 25, no. 5, pp. 553–563, 2007.
[6] M. Lades, J. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R. Wurtz, and W. Konen, "Distortion invariant object recognition in the dynamic link architectrue," IEEE Transactions on Computers, vol. 42, no. 3, pp. 300–311, 1993.
[7] L. Shen and L. Bai, "A review of Gabor wavelets for face recognition," Pattern Analysis and Applications, vol. 9, no. 2,pp.273-292,2006.
[8] V. Kyrki, J.-K. Kamarainen, and H. K¨alvi¨ainen, "SimpleGabor feature space for invariant object recognition," Pattern Recognition Letters, vol. 25, no. 3, pp. 311–318, 2004.
[9] L. Shen and L. Bai, "Information theory for Gabor feature selection for face recognition," EURASIP Journal on Applied Signal Processing, vol. 2006, Article ID 30274, 11 pages, 2006.
[10] B. Zhang, S. Shan, X. Chen, and W. Gao, "Histogram of gabor phase patterns (hgpp): A novel object representation approach for face recognition," IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 57–68, 2007.
[11] B. Kovesi, "Image features from phase congruency," Videre: Journal of Computer Vision Research, vol. 1, no. 3, pp. 1–26, 1999.
[12] V. ˇ Struc and N. Paveˇsi´c, "A palmprint verification system based on phase congruency feautres," in Proceedings of the COST 2101 Workshop on Biometrics and Identity Manegement(BIOID’08), Denmark, May 2008.
[13] P. Belhumeur, J. Hespanha, and D. Kriegman, "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection," in Proceedings of the 4th ECCV, Cambridge, UK, April 1996, pp. 45–58.
[14] V. ˇ Struc, F. Miheliˇc, and N. Paveˇsi´c, "Face authentication using a hybridapproach," Journal of Electronic Imaging, vol. 17, no. 1, 2008.
[15] http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
Author Information
  • Department of Electronique, University of Sidi Bel Abbes 22000, Algeria

  • College of Engineering, Djillali Liabes University, Sidi-Bel-Abbes 22000, Algeria

Cite This Article
  • APA Style

    Nouar Larbi, Dine Mohamed. (2013). Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition. Journal of Electrical and Electronic Engineering, 1(2), 41-45. https://doi.org/10.11648/j.jeee.20130102.11

    Copy | Download

    ACS Style

    Nouar Larbi; Dine Mohamed. Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition. J. Electr. Electron. Eng. 2013, 1(2), 41-45. doi: 10.11648/j.jeee.20130102.11

    Copy | Download

    AMA Style

    Nouar Larbi, Dine Mohamed. Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition. J Electr Electron Eng. 2013;1(2):41-45. doi: 10.11648/j.jeee.20130102.11

    Copy | Download

  • @article{10.11648/j.jeee.20130102.11,
      author = {Nouar Larbi and Dine Mohamed},
      title = {Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {1},
      number = {2},
      pages = {41-45},
      doi = {10.11648/j.jeee.20130102.11},
      url = {https://doi.org/10.11648/j.jeee.20130102.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.jeee.20130102.11},
      abstract = {The paper compares two feature extraction techniques for face recognition with Gabor Filters. Firstly Gabor Filters based methods which mainly use only Gabor magnitude features like Gabor Fisher Classifier (GFC), and secondly the proposed method called the Phase-based Gabor Fisher Classifier (PBGFC) by turk[3]. The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. In ours experiments we use the ORL data base, the feasibility of the proposed methods was assessed in a series of face verification experiments. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as (LDA), while it ensures nearly similar verification performance as the established Gabor Fisher Classifier (GFC).},
     year = {2013}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Compare Gabor Fisher Classifier and Phase-Based Gabor Fisher Classifier for Face Recognition
    AU  - Nouar Larbi
    AU  - Dine Mohamed
    Y1  - 2013/06/10
    PY  - 2013
    N1  - https://doi.org/10.11648/j.jeee.20130102.11
    DO  - 10.11648/j.jeee.20130102.11
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 41
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20130102.11
    AB  - The paper compares two feature extraction techniques for face recognition with Gabor Filters. Firstly Gabor Filters based methods which mainly use only Gabor magnitude features like Gabor Fisher Classifier (GFC), and secondly the proposed method called the Phase-based Gabor Fisher Classifier (PBGFC) by turk[3]. The PBGFC method constructs an augmented feature vector which encompasses Gabor-phase information derived from a novel representation of face images - the oriented Gabor phase congruency image (OGPCI) - and then applies linear discriminant analysis to the augmented feature vector to reduce its dimensionality. In ours experiments we use the ORL data base, the feasibility of the proposed methods was assessed in a series of face verification experiments. The experimental results show that the PBGFC method performs better than other popular feature extraction techniques such as (LDA), while it ensures nearly similar verification performance as the established Gabor Fisher Classifier (GFC).
    VL  - 1
    IS  - 2
    ER  - 

    Copy | Download

  • Sections