Face Recognition Based on Grain-Shape Features

  • Weijun Dong
  • Mingquan Zhou
  • Guohua Geng
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 125)


Traditional face recognition method was mainly dependent on single vision features such as color, texture, shape and so on. So the recognition result was not satisfactory. To cure this problem, propose a new face recognition method to get the texture features and shape features of face image based on wavelet transform. The corresponding shape and texture features are then processed by linear discriminant analysis. The PIE face database was used to test the proposed method. The experiment result shows that the proposed method has better recognizing effect and is not sensitive to the pose and expression of human faces. Experimental result also shows that the method is superior to the PCA and DCT method.


Face Recognition Texture Features Shape Feature Feature Abstraction 


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  1. 1.
    Deng, W., Hu, J., Guo, J., et al.: Emulating biological strategies for uncontrolled face recognition. Pattern Recognition 43(6), 2210–2223 (2010)MATHCrossRefGoogle Scholar
  2. 2.
    Liu, Z.M., Liu, C.J.: A hybrid color and Frequency Features method for face recognition. IEEE Transactions on Image Processing 17(10), 1975–1980 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kennedy, G.J., Orbach, H.S., Loffler, G.: Global shape versus local feature: An angle illusion. Vision Research 48(11), 1281–1289 (2008)CrossRefGoogle Scholar
  4. 4.
    Fazekas, S., Amiaz, T., Chetverikov, D., et al.: Dynamic texture detection based on motion analysis. International Journal of Computer Vision 82(1), 48–63 (2009)CrossRefGoogle Scholar
  5. 5.
    Cheng, Z.: Wavelet anlysis and applications, pp. 156–223. Xi’an Jiaotong University Press, Xi’an (1998) (in Chinese)Google Scholar
  6. 6.
    Feng, G.C., Yuen, P.C.: Multi cues eye detection on gray intensity images. Pattern Recognition 34(5), 1033–1046 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Liu, X.-D., Chen, Z.-Q.: Research on Several Key Problems in Face Recognition. Journal of Computer Research and Development 41(7), 1075–1080 (2004)Google Scholar
  8. 8.
    Bruneli, R., Poggio, T.: Face recognition: Features versus templates. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(10), 1042–1052 (1993)CrossRefGoogle Scholar
  9. 9.
    Hafed, Z.M., Levine, M.D.: Face recognition using the discrete cosine transform. International Journal of Computer Vision 43(3), 167–188 (2001)MATHCrossRefGoogle Scholar
  10. 10.
    Zhang, Y.K., Liu, C.Q.: A Novel Face Recognition Method Based on Linear Discriminant Analysis. Journal Infrared Millimeter and Waves 22(5), 327–330 (2003)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Weijun Dong
    • 1
  • Mingquan Zhou
    • 1
  • Guohua Geng
    • 1
  1. 1.College of Information Science and TechnologyNorthwest UniversityXi anChina

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