access icon free Low-complexity face recognition using contour-based binary descriptor

© The Institution of Engineering and Technology
Received 10/01/2017, Accepted 28/05/2017, Revised 11/05/2017, Published 09/06/2017

Face recognition has become a popular topic due to its applications in security, surveillance and so on. Current local methods such as the local binary pattern (LBP) or local derivative pattern (LDP) perform better than holistic methods since they are more stable on local changes such as misalignment, expression or occlusion, but their high computational complexity limit their applications. While LBP is a good feature method, the scale invariant feature transform (SIFT) is widely accepted as one of the best features to capture edge or local shape information. However, SIFT-based schemes are sensitive to illumination variation. Thus, the authors propose an LBP edge-mapped descriptor that uses maxima of gradient magnitude points. It accurately illustrates facial contours and has low computational complexity. Under variable lighting, experimental results show that the authors' method has a 16.5% higher recognition rate and requires 9.06 times less execution time than SIFT under FERET fc. Besides, when applied to the Extended Yale Face Database B, the authors' method outperformed SIFT-based approaches as well as saving about 70.9% in execution time. In uncontrolled conditions, their method has a 0.82% higher recognition rate than LDP histogram sequences in the Unconstrained Facial Images database.

Inspec keywords: transforms; face recognition; computational complexity

Other keywords: illumination variation; scale invariant feature transform; gradient magnitude points; computational complexity; local derivative pattern; unconstrained facial images database; contour-based binary descriptor; variable lighting; facial contours; LBP edge-mapped descriptor; local binary pattern; LDP; SIFT; local shape information; maxima; low-complexity face recognition

Subjects: Computer vision and image processing techniques; Computational complexity; Integral transforms; Integral transforms; Image recognition

References

    1. 1)
      • 8. Tan, X., Triggs, B.: ‘Enhanced local texture feature sets for face recognition under difficult lighting conditions’, IEEE Trans. Image Process., 2010, 19, pp. 1635–1650.
    2. 2)
      • 10. Vu, N.S., Dee, H.M., Caplier, A.: ‘Face recognition using the POEM descriptor’, Pattern Recognit., 2012, 45, pp. 2478–2488.
    3. 3)
      • 26. Lee, K.C., Ho, J., Kriegman, D.J.: ‘Acquiring linear subspace for face recognition under variable lighting’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, pp. 684–698.
    4. 4)
      • 2. Awad, A.I., Hassaballah, M.: ‘Image feature detectors and descriptors: foundations and applications’, Studies in Computational Intelligence Series, (Springer, 2016), ISBN: 978-3-319-28854-3 http://www.springer.com/us/book/9783319288529.
    5. 5)
      • 11. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, pp. 91–110.
    6. 6)
      • 12. Luo, J., Ma, Y., Takikawa, E., et al: ‘Person-specific SIFT features for face recognition’. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), HI, USA, April 2007, pp. 593–596.
    7. 7)
      • 27. Lenc, L., Král, P.: ‘Unconstrained facial images: database for face recognition under real-word conditions’. Mexican Int. Conf. Artificial Intelligence (MICAI), Cuernavaca, Mexico, October 2015, pp. 349–361.
    8. 8)
      • 17. Faraji, M., Shanbehzadeh, J., Narollahi, K., et al: ‘Extremal regions detection guided by maxima of gradient magnitude’, IEEE Trans. Image Process., 2015, 24, pp. 5401–5415.
    9. 9)
      • 1. Hassaballah, M., Aly, S.: ‘Face recognition: challenges, achievements and future directions’, IET Comput. Vis. J., 2015, 9, pp. 614–626.
    10. 10)
      • 13. Guillaumin, M., Mensink, T., Verbeek, J., et al: ‘Automatic face naming with caption-based supervision’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), AK, USA, June 2008, pp. 1–8.
    11. 11)
      • 22. Zuiderveld, K.: ‘Contrast limited adaptive histogram equalization’, in Heckbert, P.S. (Ed.): ‘Graphic Gems IV’ (San Diego, Academic Press Professional, 1994), pp. 474–485.
    12. 12)
      • 20. Ridler, T.W., Calvard, S.: ‘Picture thresholding using an iterative selection method’, IEEE Trans. Syst. Man Cybern., 1978, SMC-8, pp. 630–632.
    13. 13)
      • 21. Gonzalez, R.C., Woods, R.E: ‘Digital image processing: an adapted version’ (Pearson Education Taiwan, 2008, 3rd edn.).
    14. 14)
      • 24. Phillips, P.J., Wechsler, H., Huang, J., et al: ‘The FERET database and evaluation procedure for face-recognition algorithms’, Image Vis. Comput. J., 1998, 16, pp. 295–306.
    15. 15)
      • 29. Lenc, L., Král, P.: ‘Automatically detected feature positions for LBP based face recognition’. Artificial Intelligence Applications and Innovations (AIAI), Rhodes, Greece, September 2014, pp. 246–255.
    16. 16)
      • 9. Zhang, B., Gao, Y., Zhao, S., et al: ‘Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor’, IEEE Trans. Image Process., 2010, 19, pp. 533–544.
    17. 17)
      • 14. Vu, N.S., Caplier, A.: ‘Enhanced patterns of oriented edge magnitudes for face recognition and image matching’, IEEE Trans. Image Process., 2012, 21, pp. 1352–1364.
    18. 18)
      • 5. Lu, J., Liong, V.E., Zhou, X., et al: ‘Learning compact binary face descriptor for face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, pp. 2041–2056.
    19. 19)
      • 16. Krizaj, J., Struc, V., Pavesić, N.: ‘Adaptation of SIFT features for robust face recognition’. Int. Conf. Image Analysis and Recognition (ICIAR), Povoa de Varzim, Portugal, June 2010, pp. 394–404.
    20. 20)
      • 15. Zhang, S., Tian, Q., Lu, K., et al: ‘Edge-SIFT: discriminative binary descriptor for scalable partial-duplicate mobile search’, IEEE Trans. Image Process., 2013, 22, pp. 2899–2902.
    21. 21)
      • 4. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: ‘Eigenfaces vs. fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 7, pp. 711–720.
    22. 22)
      • 23. Ojala, T., Pietikäinen, M., Mäenpää, T.: ‘Multiresolution gray-scale and rotation invariant texture classification with local binary patterns’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, pp. 971–987.
    23. 23)
      • 3. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cogn. Neurosci., 1991, 3, pp. 71–86.
    24. 24)
      • 7. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, pp. 2037–2041.
    25. 25)
      • 25. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: ‘From few to many: illumination cone models for face recognition under variable lighting and pose’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, pp. 643–660.
    26. 26)
      • 18. Phillips, P.J., Moon, H., Rizvi, S.A., et al: ‘The FERET evaluation methodology for face-recognition algorithms’, IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22, pp. 1090–1104.
    27. 27)
      • 19. Canny, J.: ‘A computational approach to edge detection’, IEEE Trans. Pattern Anal. Mach. Intell., 1986, PAMI-8, pp. 679–698.
    28. 28)
      • 28. Shen, Y.K., Chiu, C.T.: ‘Local binary pattern orientation based face recognition’. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Queensland, Australia, April 2015, pp. 1091–1095.
    29. 29)
      • 6. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A Comparative study of texture measures with classification based on feature distributions’, Pattern Recognit., 1996, 29, pp. 51–59.
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