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Comparison of Multi-View Face Recognition using DCT and Hybrid DWT of Score Fusion under Uncontrolled Illumination Variation

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 4
Year of Publication: 2014
Authors:
Manisha J Kasar
Nitin S. Choubey
10.5120/16784-6369

Manisha J Kasar and Nitin S Choubey. Article: Comparison of Multi-View Face Recognition using DCT and Hybrid DWT of Score Fusion under Uncontrolled Illumination Variation. International Journal of Computer Applications 96(4):37-44, June 2014. Full text available. BibTeX

@article{key:article,
	author = {Manisha J Kasar and Nitin S. Choubey},
	title = {Article: Comparison of Multi-View Face Recognition using DCT and Hybrid DWT of Score Fusion under Uncontrolled Illumination Variation},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {4},
	pages = {37-44},
	month = {June},
	note = {Full text available}
}

Abstract

A facial recognition system is one of the biometric applications for automatically identifying or verifying a person from a digital image or a video frame from a video source. Now a day's face recognition system is recognize the face using multiple-views of faces, for detecting each view of face such as left, right, front, top, and bottom. The proposed face recognition system consists of a novel illumination-insensitive preprocessing method, a hybrid discrete Wavelet Transform (HDWT) for feature extraction and Density based Score Fusion Technique for matching. First, in the preprocessing stage different stages are followed like Gamma Correction, DOG(Difference of Gaussion)filtering and contrast equalization which transforms, a face image into an illumination-insensitive image, Then, for feature extraction of complementary classifiers, multiple face models based upon HDWT are applied. To do the feature extraction this system uses DCT Wavelet transform to generate the feature vectors of the query and database images. Euclidean distance is used as similarity measure to compare the image. Finally, to combine scores from multiple complementary classifiers, a log likelihood ratio-based score fusion scheme is applied. The proposed system uses the ORL face database, and compare with DCT method. Using ORL database the Hybrid wavelet has achieves smaller size feature vectors which is performing better with 98. 55% accuracy and less computation time require for recognizing image which is 19. 0645 msec to 24. 7653 msec . Which is less as compare to DCT method, DCT method achieves 96. 45% accuracy and computational time 26. 2349 msec to 33. 0234 msec which is more as compare to proposed method. .

References

  • Wei-Lun Chao, "Face Recognition", GICE, National Taiwan University, March 2007.
  • Ravi Ramamoorthi and Pat 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. 2448–2459,2001.
  • Amnon Shashua and Tammy Riklin-Raviv," The quotient image: Class-based re-rendering and recognition with varying illuminations", IEEE Trans. Pattern Anal. Mach. Intell. , vol. 23, no. 2, pp. 129–139,2002.
  • H. Wang, S. Li, and Y. Wang," Generalized quotient image", IEEE. Comput. Vis. Pattern Recognit. , vol. 2, pp. 498–505,2004.
  • M. Savvides, B. Kumar, and P. Khosla," Corefaces Robust shift invariant PCA based correlation filter for illumination tolerant face recognition", IEEE, Comput. Vis. Pattern Recognit. , vol. 2, pp. 834–841, 2004.
  • Anil Jain, Karthik Nandakumar , Arun Ross," Score normalization in multimodal biometric systems", Science Direct,Pattern Recognition 38,2270 – 2285,2005.
  • C. Xie, M. Savvides, and B. V. Kumar," Kernel Correlation Filter Based Redundant Class-Dependence Feature Analysis (KCFA) on FRGC2. 0 Data", Springer-Verlag Berlin Heidelberg, AMFG 2005, LNCS 3723, pp. 32–43, 2005.
  • Xiaoyang Tan and Bill Triggs," Fusing gabor and LBP feature set for kernel-based face recognition", IEEE Int. Workshop Anal. Model. Face Gestures, pp. 235–249, 2007.
  • Karthik Nandakumar," Likelihood Ratio Based Biometric Score Fusion", To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.
  • Jian Yang , Chengjun Liu," Color image discriminant models and algorithms for face recognition", IEEE Trans. Neural Netw. , vol. 19, no. 12, pp. 2088–2098,2008.
  • Zhiming Liu, Chengjun Liu," Robust face recognition using color information", Advance Biometric, vol. 5558/2009, pp. 122–131,2009.
  • Yu Su, Shiguang Shan, X. Chen, and W. Gao," Hierarchical ensemble of global and local classifiers for face recognition", IEEE Trans. Image Process. , vol. 18, no. 8, pp. 1885–1896,2009.
  • Wonjun Hwang, Haitao Wang, Hyunwoo Kim," Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation", IEEE Transactions On Image Processing, Vol. 20, No. 4,2011
  • M. M. Mohie El-Din1, M. Y. El Nahas2 and H. A. El Shenbary," Hybrid Framework for Robust Multimodal Face Recognition", IJCSI International Journal of Computer Science Issues, Vol. 10, Issue 2, No 2,2013.
  • Radoslav Vargic," DISCRETE Hybrid wavelet TRANSFORMS and their use for image compression", Department of telecommunications, FEI STU, Ilkovi?ova 3, 812 19 Bratislava, Slovakia,2013.
  • S. Anila ,Dr. N. Devarajan," Preprocessing Technique for Face Recognition Applications under Varying Illumination Conditions", Global Journal of Computer Science and Technology Graphics & Vision ,Volume 12 Issue 11 Version 1. 0 Year 2012.
  • R. J. E. Merry," Wavelet Theory and Applications A literature study", Eindhoven University of Technology Department of Mechanical Engineering Control Systems Technology Group Eindhoven, June 7, 2005.
  • H. B. kekre and Kavita Sonawane," Retrieval of Images Using DCT and DCT Wavelet Over Image Blocks", International Journal of Advanced Computer Science and Applications, Vol. 2, No. 10, 2011.
  • F. S. Samaria and A. C. Harter, "Parameterisation of a stochastic model for human face identification", Proc. of 2nd IEEE workshop on Applications of Computer Vision, pp. 138-142, 1994.
  • Derzu Omaia , JanKees v. d. Poel, Leonardo V. Batista, "2D-DCT Distance Based Face Recognition Using a Reduced Number of Coefficients", PPGI / DI / UFPB PPGEM / DEM / UFPB Brasil,2008.