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Selective local texture features based face recognition with single sample per class


Local appearance-based methods have been successfully applied to face recognition and achieved state-of-the-art performance. In this paper we propose a local selective feature extraction approach based on Gabor filters and the Local Binary Pattern (LBP) approach to face recognition. A Gabor filter extracts the textural features from the face image and generates the binary face template using those features. The binary face template acts like a mask to extract the local texture information of the face image using a Local Binary Pattern technique. This selective local texture feature approach uses the histogram-based matching for face recognition. This method reduces the computation time considerably. This method also reduces the number of Local Binary Patterns into half compared to the existing LBP method. This proposed approach reduces the computation time for the FERET dataset by 45%. Experiments on well-known face databases such as FERET, Yale, Indian Faces and ORL show that this approach obtains consistent and promising results in the scenario of one training sample per person with significant facial variation.


  1. 1.

    Ahonen T, Hadid A, Pietikainen M (2004) Face recognition with Local Binary Patterns. In: Proceedings of the 8th European conference on computer vision (ECCV ’04), Prague, Czech Republic, May 11–14. vol 1, pp 469–481

    Google Scholar 

  2. 2.

    Ahonen T, Hadid A, Pietikainen M (2006) Face description with Local Binary Patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    Article  Google Scholar 

  3. 3.

    Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  4. 4.

    Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  5. 5.

    Chen S, Liu J, Zhou ZH (2004) Making FLDA applicable to face recognition with one sample per person. Pattern Recognit 37(7):1553–1555

    MathSciNet  Article  Google Scholar 

  6. 6.

    Chen S, Zhang D, Zhou ZH (2004) Enhanced (PC) A for face recognition with one training image per person. Pattern Recognit Lett 25(10):1173–1181

    MathSciNet  Article  Google Scholar 

  7. 7.

    Etemad K, Chellappa R (1997) Discriminant analysis for recognition of human face images. J Opt Soc Am A 14(8):1724–1733

    Article  Google Scholar 

  8. 8.

    Geng X, Zhou ZH (2006) Image region selection and ensemble for face recognition. J Comput Sci Technol 21(1):116–125

    MathSciNet  Article  Google Scholar 

  9. 9.

    He X, Yan X, Hu Y, Niyogi P, Zhang H (2005) Face recognition using Laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340

    Article  Google Scholar 

  10. 10.

    Heisele P, Ho J, Wu X, Poggio T (2003) Face recognition: component-based versus global approaches. Comput Vis Image Underst 91(1):6–12

    Article  Google Scholar 

  11. 11.

    Face Indian Database (2011)

  12. 12.

    Jaya Priya K, Rajesh RS (2010) Dual tree complex wavelet transform based face recognition with single view. Ubiquitous Comput Commun J 5(1)

  13. 13.

    Jaya Priya K, Rajesh RS (2010) Local fusion of complex dual-tree wavelet coefficients based face recognition for single sample problem. Proc Comput Sci 2(1):94–100

    Article  Google Scholar 

  14. 14.

    Lawrence S, Lee Giles C, Tsoi A, Back A (1997) Face recognition: a convolutional neural-network approach. IEEE Trans Neural Netw 8(1):98–113

    Article  Google Scholar 

  15. 15.

    Lei Z, Li SZ, Chu R, Zhu X (2007) Face recognition with local Gabor textons. In: Lecture Notes in Computer Science, vol 4642. Springer, Berlin, pp 49–57

    Google Scholar 

  16. 16.

    Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476

    Article  Google Scholar 

  17. 17.

    Martinez M (2002) Recognizing imprecisely localized partially occluded, and expression variant faces from a single sample per class. IEEE Trans Pattern Anal Mach Intell 24(6):748–763

    Article  Google Scholar 

  18. 18.

    Mika S, Ratsch G, Weston J, Scholkopf B, Muller KR (1999) Fisher discriminant analysis with kernels. In: Proceedings of Neural Networks for Signal Processing IX, pp 41–48

    Google Scholar 

  19. 19.

    Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognit 29(1):51–59

    Article  Google Scholar 

  20. 20.

    Ojala T, Pietikainen M, Maenpaa T (2010) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  21. 21.

    ORL (2011)

  22. 22.

    Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306

    Article  Google Scholar 

  23. 23.

    Scholkopf B, Smola A, Muller KR (1999) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  24. 24.

    Tan X, Chen S, Zhou ZH, Zhang F (2005) Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble. IEEE Trans Neural Netw 16(4):875–886

    Article  Google Scholar 

  25. 25.

    Tan X, Chen S, Zhou ZH, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recognit 39(1):1725–1745

    MATH  Article  Google Scholar 

  26. 26.

    Turk M, Pentland A, Neurosci J (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  27. 27.

    Wang X, Tang X (2004) A unified framework for subspace face recognition. IEEE Trans Pattern Anal Mach Intell 26(9):1222–1228

    Article  Google Scholar 

  28. 28.

    Hu W, Li X, Zhongfei Z, Wang H (2010) Heat kernel based local binary pattern for face representation. IEEE Signal Process Lett 17(3):308–311

    Article  Google Scholar 

  29. 29.

    Zhang W, Shan S, Gao W, Chen X, Zhang H, Wang J (2005) Local Gabor Binary Pattern Histogram Sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Proceedings of the 10th international conference on computer vision, Beijing, China, October 17–21, vol 1, pp 786–791

    Google Scholar 

  30. 30.

    Wiskott L, Fellous MJ, Kruger N, Von der Malsburg C (1997) Face recognition by elastic bunch graph matching. IEEE Trans Pattern Anal Mach Intell 19(7):775–779

    Article  Google Scholar 

  31. 31.

    Xiang C, Fan AX, Lee HT (2006) Face recognition using recursive fisher linear discriminant. IEEE Trans Image Process 15(8):2097–2105

    Article  Google Scholar 

  32. 32.

    Yale (2011)

  33. 33.

    Yang J, Frangi F, Yang A, Zhang D, Jin Z (2005) KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition. IEEE Trans Pattern Anal Mach Intell 27(2):230–244

    Article  Google Scholar 

  34. 34.

    Zhang B, Shan S, Chen X, Gao W (2007) Histogram of Gabor phase patterns (hgpp): a novel object representation approach for face recognition. IEEE Trans Image Process 16(1):57–68

    MathSciNet  Article  Google Scholar 

  35. 35.

    Zhang B, Shan S, Chen X, Gao W (2009) Are Gabor phases really useless for face recognition? PAA Pattern Anal Appl 12(3):301–307

    MathSciNet  Article  Google Scholar 

  36. 36.

    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput 35(4):399–458

    Article  Google Scholar 

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Correspondence to K. Jaya Priya.

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Jaya Priya, K., Rajesh, R.S. Selective local texture features based face recognition with single sample per class. J Braz Comput Soc 18, 229–235 (2012).

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  • Face recognition
  • Local Binary Pattern
  • Gabor filter
  • Binary face template
  • Expression invariant face recognition
  • Single Sample Problem