Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination

Adaptively weighted orthogonal gradient binary pattern for single sample face recognition under varying illumination

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To overcome the limitation of traditional illumination invariant methods for single sample face recognition, a modified version of gradientface named adaptively weighted orthogonal gradient binary pattern (AWOGBP), which is proved robust to illumination variation, is proposed in this study. First, the Tetrolet transform is performed on the images to obtain low frequency and high frequency components and the retina model processing is applied to low frequency component to make the image more robust to illumination, in the meantime, the authors multiply each element in high frequency components with a scale factor to accentuate details. Then the proposed AWOGBP is used to get the feature vectors of each direction and all the feature vectors are concatenated into the general feature vector for face recognition with the weights of the sub-graph based on their information entropy which is defined as the contribution to describe the whole face images. Finally the principle component analysis method is used to reduce dimensions and the nearest neighbour classifier is used for face image classification and recognition. Experimental results on CMU PIE and Extended Yale B face databases indicate that the proposed method is significantly better as compared with related state-of-the-art methods.


    1. 1)
      • 1. Faraji, M.R., Qi, X.: ‘An effective neutrosophic set-based preprocessing method for face recognition’. 2013 IEEE Int. Conf. on Multimedia and Expo Workshops (ICMEW), 2013, vol. 370, pp. 14.
    2. 2)
    3. 3)
      • 3. Shafie, A.A., Hafiz, F., Mustafah, Y.M.: ‘Face recognition using illumination-invariant local patches’. 2014 5th Int. Conf. on Intelligent and Advanced Systems (ICIAS), 2014, pp. 16.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • 12. Vu, N.S., Caplier, A.: ‘Illumination-robust face recognition using retina modeling’. 2009 16th IEEE Int. Conf. on Image Processing (ICIP) IEEE, 2009, pp. 32893292.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
      • 18. Jabid, T., Kabir, M.H., Chae, O.: ‘Local directional pattern (LDP) for face recognition’. IEEE Int. Conf. Consumer Electronics 2010, 2010, pp. 329330.
    19. 19)
    20. 20)
    21. 21)
      • 21. Essa, A.E., Asari, V.K.: ‘Local directional pattern of phase congruency features for illumination invariant face recognition’, Opt. Pattern Recogn. XXV, 2014, 9094, (7), pp. 11871190.
    22. 22)
    23. 23)
    24. 24)
    25. 25)
    26. 26)
    27. 27)
      • 27. Golomb, S.W.: ‘Polyominoes’ (Princeton University Press, 1994).
    28. 28)
      • 28. Larsson, B.: ‘Problem 2623’, Fairy Chess Rev., 1937, 3, (5), p. 51.
    29. 29)
      • 29. Lei, W., Shen, T.-Z.: ‘Two-dimensional entropy method based on genetic algorithm’, J. Beijing Inst. Technol., 2002, 11, (2) (32), pp. 184188.
    30. 30)
      • 30. Khan, R.A., Meyer, A., Konik, H., et al: ‘Facial expression recognition using entropy and brightness features’. 2011 11th Int. Conf. on Intelligent Systems Design and Applications (ISDA), IEEE, 2011, pp. 737742.
    31. 31)
      • 31. Kanan, H.R., Moin, M.S.: ‘Face recognition using entropy weighted patch Pca array under variation of lighting conditions from a single sample image per person’. Int. Conf. on Information Communications and Signal Processing ICICS.2009, pp. 15.
    32. 32)
      • 32. Imtiaz, H., Fattah, S.A., Hope, M., et al: ‘A contourlet-domain feature extraction scheme for face recognition’, Int. J. Recent Trends Eng. Technol., 2011, 6, (2), pp. 9–13.
    33. 33)
      • 33. Sim, T., Baker, S., Bsat, M.: ‘The CMU Pose, Illumination, and Expression (PIE) Database’. Proc. Conf. Automatic Face and Gesture Recognition, Washington, 2002.

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