| |
| |
|
|
|
|
| A Spectral Domain Local Feature Extraction Algorithm for Face Recognition
|
|
Full
text: |
 PDF(987.4KB) |
|
|
Source |
International Journal of Security (IJS) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(1.81MB) |
|
Volume: 5 Issue: 2 |
| |
Pages: NULL |
|
Publication
Date: July / August 2011 |
|
ISSN
(Online): 1985-2320 |
|
|
|
|
|
Pages |
62 - 73 |
|
Author(s) |
|
|
|
Published
Date |
05-08-2011 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Feature Extraction, Classification, Two Dimensional Discrete Fourier Transform, Dominant Spectral Feature, Face Recognition, Modularization |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Directory of Open Access Journals (DOAJ) |
| 2. Google Scholar |
| 3. Scribd |
| 4. Docstoc |
| |
|
| |
|
|
| In this paper, a spectral domain feature extraction algorithm for face recognition is proposed, which efficiently exploits the local spatial variations in a face image. For the purpose of feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal bands from the face image. In order to capture the local variations within these high-informative horizontal bands precisely, a feature selection algorithm based on two-dimensional discrete Fourier transform (2D-DFT) is proposed. Magnitudes corresponding to the dominant 2D-DFT coefficients are selected as features and shown to provide high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentations have been carried out upon standard face databases and the recognition performance is compared with some of the existing face recognition schemes. It is found that the proposed method offers not only computational savings but also a very high degree of recognition accuracy. |
| |
|
| |
|
| |
| 1 |
A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4 – 20, 2004.
|
|
|
| 2 |
C.Villegas-Quezada and J. Climent, “Holistic face recognition using multivariate approximation, genetic algorithms and adaboost classi?er: Preliminary results,” World Academy of Science, Engineering and Technology, vol. 44, pp. 802–806, 2008.
|
|
|
| 3 |
L. L. Shen and L. Bai, “Gabor feature based face recognition using kernal methods,” in Proc. IEEE Int. Conf. Automatic Face and Gesture Recognition, vol. 6, 2004, pp. 386–389.
|
|
|
| 4 |
M. Zhou and H. Wei, “Face veri?cation using gabor wavelets and adaboost,”in Proc. Int. Conf. Pattern Recognition, vol. 1, 2006, pp. 404–407.
|
|
|
| 5 |
C. BenAbdelkader and P. Grif?n, “A local region-based approach to gender classi?cation from face images,” in Proc. IEEE Comp. Society Conf. Computer Vision and Pattern Recognition, vol. 3, 2005, pp. 52–57.
|
|
|
| 6 |
R. Gottumukkal and V. K. Asari, “An improved face recognition technique based on modular PCA approach,” Pattern Recognition Lett., vol. 25, pp. 429–436, 2004.
|
|
|
| 7 |
S. Alirezaee, H. Aghaeinia, K. Faez, and F. Askari, “An ef?cient algorithm for face localization,” Int. Journal of Information Technology, vol. 12, pp. 30–36, 2006.
|
|
|
| 8 |
E. Loutas, I. Pitas, and C. Nikou, “Probabilistic multiple face detection and tracking using entropy measures,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, pp. 128–135, 2004.
|
|
|
| 9 |
S. C. Dakin and R. J. Watt, “Biological ‘bar codes’ in human faces,” World Academy of Science, Engineering and Technology, vol. 9, pp. 1–10, 2009.
|
|
|
| 10 |
X. Zhang and Y. Gao, “Face recognition across pose: A review,” Pattern Recogn., vol. 42, pp. 2876–2896, 2009.
|
|
|
| 11 |
R. C. Gonzalez and R. E. Woods, Digital Image Processing. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc., 1992.
|
|
|
| 12 |
I. Jolloffe, “Principal component analysis,” Springer-Verlag, Berlin, 1986.
|
|
|
| 13 |
F. M. deS. Matos, L.V. Batista, and J.v.d. Poel, “Face recognition using DCT coef?cients selection,” in Proc. ACM symp. Applied computing, 2008, pp. 1753–1757.
|
|
|
| 14 |
X.Y. Jing and D. Zhang, “A face and palm print recognition approach based on discriminant DCT feature extraction,” IEEE Trans. Systems, Man, and Cybernetics, vol. 34, pp. 2405–2415, 2004.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Shaikh Anowarul Fattah : Colleagues
|
|
| Hafiz Imtiaz : Colleagues
|
|
|
|
|
|
|
|
|
|
|
