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

Nouar Larbi; Dine Mohamed

doi:doi:10.11648/j.jeee.20130102.11

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

Nouar Larbi, Dine Mohamed

Published in Journal of Electrical and Electronic Engineering (Volume 1, Issue 2)

Received: 11 May 2013 Published: 10 June 2013

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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).

Published in	Journal of Electrical and Electronic Engineering (Volume 1, Issue 2)
DOI	10.11648/j.jeee.20130102.11
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), 2013. Published by Science Publishing Group

Keywords

Face Recognition, Gabor Filter, Gabor Phase Congruency

References

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[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.

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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

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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

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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

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@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://article.sciencepublishinggroup.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}
}

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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  -

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Author Information

Nouar Larbi

Department of Electronique, University of Sidi Bel Abbes 22000, Algeria
Dine Mohamed

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

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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

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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

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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://article.sciencepublishinggroup.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