Open Access Open Access  Restricted Access Subscription or Fee Access

Illumination and Expression Invariant Face Recognition System Using Discrete Wavelet and Hybrid Fourier Feature with Multiple Face Model

R. Sridevi, K. Priyadharshini, S. Kalaivani, R. Sridhar


Abstract---Face recognition is one of the challenging applications of image processing. It has been actively investigated by the scientific community and has already taken its place in modern consumer software. However, there are still major challenges remaining e.g. variations in pose, lighting and appearance, even with well known face recognition systems. In this paper, we present a Robust face recognition system should posses the ability to recognize identity despite many variations in pose, lighting and appearance.
This proposed face recognition system consists of a novel
illumination-insensitive preprocessing method, a combined method of feature extraction using discrete wavelet Transform and hybrid fourier transform with multiple face model to improve the
recognition rate, and a score fusion scheme. First, in the
preprocessing stage, a face image is transformed into an illuminationinsensitivimage, called an ―integral normalized gradient image,‖ by
normalizing and integrating the smoothed gradients of a facial image.Then, for feature extraction of complementary classifiers,multipleface models based upon discrete wavelet and hybrid fourier featuresare applied. The wavelet featuresare extracted from wavelet
coefficient values with the same size as the original image. Thesecoefficients are used to describe the face image. The hybrid Fouriereatures are extracted from different Fourier domains in differentfrequency bandwidths and then each feature is individually classified
by linear discriminant analysis. In addition, multiple face models are enerated by plural normalized face images that have different eye distances. Finally, to combine scores from multiple complementarym classifiers, a log likelihood ratio-based score fusion scheme is applied.


Face Recognition, Integral Normalized Gradient Image Method, Discrete Wavelet and Hybrid Fourier Feature Extraction and Score Fusion.

Full Text:



Wonjun Hwang, Haitao Wang, Hyunwoo Kim , Seok-Cheol Kee, and Junmo Kim, ―Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation,‖ IEEE Trans on image processing , vol. 20, no. 4,Apr. 2011.

S. T. Gandhe, K. T. Talele, and A.G.Keskar, 2008, "Face Recognition Using Contour Matching", IAENG International Journal of Computer Science, Vol. 35, No. 2, 2008.

M. Almas Anjum, M. Younus Javed, and A. Basit, 2006, "A New Approach to Face Recognition Using Dual Dimension Reduction", International Journal of Signal Processing Vol. 2, No.1, 2006, pp1-6.

D. M. Blackburn, M. Bone, and P. J. Phillips, ―Facial recognition vendor test 2000 evaluation report,‖ Dec. 2000 [Online]. Available:

P. N. Belhumeur and D. J. Kriegman, ―What is the set of images of an object under all possible lighting conditions?,‖ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 1996, pp. 270–277.

R. Ramamoorthi and P. Hanrahan, ―On the relationship between radiance

and irradiance: Determining the illumination from images of a

convex Lambertian object,‖ J. Opt. Soc. Amer. , vol. 18, no. 10, pp.

–2459, 2001.

A. Shashua and T. Riklin-Raviv, ―The quotient image: Class-based rerendering

and recognition with varying illuminations,‖ IEEE Trans.

Pattern Anal. Mach. Intell. , vol. 23, no. 2, pp. 129–139, Feb. 2001.

M. A. Turk and A. P. Pentland, ―Eigenfaces for recognition,‖ J. Cogn. Neurosci. , vol. 3, no. 1, pp. 71–86, 1991.

P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, ―Eigenface vs. fisherfaces: Recognition using class specific linear projection,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 19, no. 7, pp. 711–720, Jul. 1997.

B. V. Kumar, M. Savvides, K. Venkataramani, and X. Xie, ―Spatial frequency domain image processing for biometric recognition,‖ in Proc.

IEEE. Intl. Conf. Image Process., 2002, vol. 1, pp. 53–56.

M. Savvides, B. Kumar, and P. Khosla, ―Corefaces—Robust shift invariant PCA based correlation filter for illumination tolerant face recognition,‖ in Proc. IEEE, Comput. Vis. Pattern Recognit. , Jun. 2004, vol. 2, pp. 834–841.

Advanced Face Descriptor Using Fourier and Intensity LDA Features , ISO/IEC JTC1/SC29/WG11-MPEG-8998, Oct. 2002.

P. Nicholl and A. Amira, ―DWT/PCA face recognition using automatic coefficient selection,‖ in Proceedings of the 4th IEEE International Workshop on Electronic Design, Test and Applications (DELTA ’08), pp. 390–393, Hong Kong, January 2008.

L. Shen, Z. Ji, L. Bai, and C. Xu, ―DWT based HMM for face recognition,‖ Journal of Electronics, vol. 24, no. 6, pp. 835–837, 2007.

A. Jain, K. Nandakumar, and A. Ross, ―Score normalization in multimodal biometric systems,‖ Pattern Recognit., vol. 38, no. 12, pp. 2270– 2285, Dec. 2005.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.