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Volume 28 Issue 3
Sep.  2010
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Liu Sheng, Xiang Jingcheng . RANGE SUPER-RESOLUTION BASED ON PULSE COMPRESSION[J]. Journal of Electronics & Information Technology, 1998, 20(3): 330-335.
Citation: Cao Lin, Wang Dong-feng, Liu Xiao-jun, Zou Mou-yan . Face Recognition Based on Two-Dimensional Gabor Wavelets[J]. Journal of Electronics & Information Technology, 2006, 28(3): 490-494.

Face Recognition Based on Two-Dimensional Gabor Wavelets

  • Received Date: 2004-08-09
  • Rev Recd Date: 2005-01-11
  • Publish Date: 2006-03-19
  • A new approach based on two-dimensional Gabor wavelets transform for face recognition is presented. The Gabor wavelet representation of an image is the convolution of the image with a family of Gabor kernels. A set of vectors called nodes, over a dense grid of image points are formed, and each node is labeled with a set of complex Gabor wavelets coefficients. The magnitudes of the coefficients are used for recognition. Principal component analysis is a decorrelation technique and its primary goal is to project the high dimensional vectors into a lower dimensional space. Feature nodes, as observation vectors of HMM, is derived by using principal component analysis. A set of images representing different instances of the same person is used to train each HMM, and each individual in the database is represented by an optimal HMM face model. Experimental results show that the proposed algorithm has a high recognition rate with relatively low complexity.
  • Gong S, Psarrou A. Dynamic Vision: from Images to Face Recognition[M]. London: Imperial College Press, 2000: 5.20.[2]Daugman J. Two-dimensional spectral analysis of cortical receptive field profiles[J]. Vision Research, 1980, 20(10): 847856. .[3]Lades M, Vorbruggen J C, Buhmann J. Distortion invariant object recognition in the dynamic link architecture[J]. IEEETrans. on computers, 1993, 42(3): 300311. .[4]Rabiner L. A tutorial on hidden Markov models and selected application in speech recognition[J].Proce. IEEE.1989,77(2):257-[5]Nefian A. A hidden Markov model-based approach for face detection and recognition[D/D]. Georgia: Georgia Institute of Technology, 1999: 38.108.[6]Othman H, Aboulnasr T. A separable low complexity 2D HMM with application to face recognition[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2003, 25(10):1229-[7]Helmuth L. Objection recognition: where the brain tells a face from a place[J]. Science, 2001, 292(5515): 196198. .[8]Duda R, Hart P, Stork D. Pattern Classification, second edition[M]. New York: Wiley-Interscience, 2000: 114.139.[9]Samaria F. Face recognition using hidden Markov model[D/D]. Cambridge: University of Cambridge, 1994: 2782.
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