access icon free Genetic-based feature fusion in face recognition using arithmetic coded local binary patterns

© The Institution of Engineering and Technology
Received 14/03/2020, Accepted 02/11/2020, Revised 29/10/2020, Published 04/12/2020

Local binary patterns (LBPs) are one of the attempts for gathering local features with face recognition algorithms. Although the application of LBP's in many recognition contents is too apparent, these methods have limited accuracy because of their threshold value. One problem is earning one value for two different regions with a diverse pixel neighbourhood, which causes mistakes in feature vector and decreases the discriminative power. In this study, the authors proposed a modified LBP that covers the LBP's disadvantages. The proposed approach is arithmetic coded LBP (ACLBP) that uses arithmetic coding process during LBP calculation instead of applying original thresholds. The proposed policy addresses the problem of returning one similar LBP value for two different patches. Moreover, the proposed method modifies LBP by using a different threshold for calculating the pixels differences. Using this algorithm, the authors conducted a genetic-based feature fusion method by combining LBP and histogram of oriented gradients and ACLBP. The proposed approach could work better on LFW dataset, and the ORL dataset and Yale face dataset that shows the improving role of ACLBP in comparison with the earlier version of LBP.

Inspec keywords: genetic algorithms; image colour analysis; image fusion; feature extraction; image classification; face recognition; image texture; gradient methods; image coding; arithmetic codes

Other keywords: original thresholds; different threshold; pixels differences; feature vector; LBP's disadvantages; threshold value; local features; arithmetic coding process; ACLBP; different patches; similar LBP value; local binary patterns; LBP calculation; face recognition algorithms; recognition contents; diverse pixel neighbourhood; modified LBP; fusion method

Subjects: Optimisation techniques; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Image recognition; Image and video coding; Optimisation techniques; Computer vision and image processing techniques

References

    1. 1)
      • 9. Bicego, M., Lagorio, A., Grosso, E., et al: ‘On the use of SIFT features for face authentication’. 2006 Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW'06), New York, NY, USA, 2006, p. 35.
    2. 2)
      • 46. Said, A.: ‘Introduction to arithmetic coding-theory and practice’, Hewlett Packard Lab. Rep., 2004, pp. 1057–7149.
    3. 3)
      • 29. Singh, C., Walia, E., Kaur, K.P.: ‘Color texture description with novel local binary patterns for effective image retrieval’, Pattern Recognit., 2018, 76, pp. 50–68.
    4. 4)
      • 15. Jun, B., Kim, D.: ‘Robust face detection using local gradient patterns and evidence accumulation’, Pattern Recognit., 2012, 45, (9), pp. 3304–3316.
    5. 5)
      • 20. Rao, M.K., Swamy, K.V.: ‘Face recognition using DWT and eigenvectors’. 2012 1st Int. Conf. on Emerging Technology Trends in Electronics, Communication & Networking, Surat, Gujarat, India, 2012, pp. 1–4.
    6. 6)
      • 56. Pillai, A., Soundrapandiyan, R., Satapathy, S., et al: ‘Local diagonal extrema number pattern: A new feature descriptor for face recognition’, Future Gener. Comput. Syst., 2018, 81, pp. 297–306.
    7. 7)
      • 7. Lu, J., Tan, Y.-P., Wang, G.: ‘Discriminative multimanifold analysis for face recognition from a single training sample per person’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 35, (1), pp. 39–51.
    8. 8)
      • 70. Guo, Z., Zhang, Y.: ‘A sparse corruption non-negative matrix factorization method and application in face image processing & recognition’, Measurement, 2019, 136, pp. 429–437.
    9. 9)
      • 30. Lee, S.H., Choi, J.Y., Ro, Y.M., et al: ‘Local color vector binary patterns from multichannel face images for face recognition’, IEEE Trans. Image Process., 2011, 21, (4), pp. 2347–2353.
    10. 10)
      • 64. Liu, Z., Wu, X.-J., Shu, Z.: ‘Sparsity augmented discriminative sparse representation for face recognition’, Pattern Anal. Appl., 2019, 22, (4), pp. 1527–1535.
    11. 11)
      • 18. 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., 2009, 19, (2), pp. 533–544.
    12. 12)
      • 21. Xi, M., Chen, L., Polajnar, D., et al: ‘Local binary pattern network: a deep learning approach for face recognition’. 2016 IEEE int. Conf. on Image processing (ICIP), Phoenix, AZ, USA, 2016, pp. 3224–3228.
    13. 13)
      • 19. Vyas, A., Yu, S., Paik, J.: ‘Multiscale transforms with application to image processing’ (Springer, Singapore, 2018).
    14. 14)
      • 47. Sayood, K.: ‘Introduction to data compression’ (Morgan Kaufmann, Netherlands, 2017).
    15. 15)
      • 61. Vu, N.-S., Dee, H.M., Caplier, A.: ‘Face recognition using the POEM descriptor’, Pattern Recognit., 2012, 45, (7), pp. 2478–2488.
    16. 16)
      • 67. Kumar, A., Rai, P., Daume, H.: ‘Co-regularized multi-view spectral clustering’. Advances in Neural Information Processing Systems, Granada, Spain, 2011, pp. 1413–1421.
    17. 17)
      • 12. Zhao, W., Chellappa, R., Phillips, P.J., et al: ‘Face recognition: a literature survey’, ACM Comput. Surv., 2003, 35, (4), pp. 399–458.
    18. 18)
      • 27. Liao, S., Law, M.W.K., Chung, A.C.S.: ‘Dominant local binary patterns for texture classification’, IEEE Trans. Image Process., 2009, 18, (5), pp. 1107–1118.
    19. 19)
      • 54. AT & T Lab.: ‘ORL database of faces’, Cambrage University, 1994, http://cam-orl.co.uk/facedatabase.html, (Accessed April 14, 2020).
    20. 20)
      • 1. Dang, K., Sharma, S.: ‘Review and comparison of face detection algorithms’. 2017 7th Int. Conf. on Cloud Computing, Data Science & Engineering-Confluence, Noida, India, 2017, pp. 629–633.
    21. 21)
      • 60. De Marsico, M., Nappi, M., Riccio, D., et al: ‘Robust face recognition for uncontrolled pose and illumination changes’, IEEE Trans. Syst. man, Cybern. Syst., 2012, 43, (1), pp. 149–163.
    22. 22)
      • 69. Wang, H., Peng, J., Fu, X.: ‘Co-regularized multi-view sparse reconstruction embedding for dimension reduction’, Neurocomputing, 2019, 347, pp. 191–199.
    23. 23)
      • 33. Jabid, T., Kabir, M.H., Chae, O.: ‘Local directional pattern (LDP) for face recognition’. 2010 Digest Of Technical Papers Int. Conf. On Consumer Electronics (ICCE), Las Vegas, NV, USA, 2010, pp. 329–330.
    24. 24)
      • 57. Shih, J.-L., Lee, C.-H., Wang, J.T.: ‘A new 3D model retrieval approach based on the elevation descriptor’, Pattern Recognit., 2007, 40, (1), pp. 283–295.
    25. 25)
      • 50. Witten, I.H., Neal, R.M., Cleary, J.G.: ‘Arithmetic coding for data compression’, Commun. ACM, 1987, 30, (6), pp. 520–540.
    26. 26)
      • 65. Truong, H. P., Kim, Y.-G.: ‘Enhanced line local binary patterns (EL-LBP): an efficient image representation for face recognition’. Int. Conf. on Advanced Concepts for Intelligent Vision Systems, Poitiers, France, 2018, pp. 285–296.
    27. 27)
      • 17. Liao, S., Zhu, X., Lei, Z., et al: ‘Learning multi-scale block local binary patterns for face recognition’. Int. Conf. on Biometrics, Seoul, Republic of Korea, 2007, pp. 828–837.
    28. 28)
      • 49. Amin, H.U., Yusoff, M.Z., Ahmad, R.F.: ‘A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques’, Biomed. Signal Process. Control, 2020, 56, p. 101707.
    29. 29)
      • 2. Kakadiaris, I.A., Toderici, G., Evangelopoulos, G., et al: ‘3d-2D face recognition with pose and illumination normalization’, Comput. Vis. Image Underst., 2017, 154, pp. 137–151.
    30. 30)
      • 16. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face recognition with local binary patterns’. European Conf. On Computer Vision, Prague, Czech Republic, 2004, pp. 469–481.
    31. 31)
      • 41. El Khadiri, I., Kas, M., El Merabet, Y., et al: ‘Repulsive-and-attractive local binary gradient contours: new and efficient feature descriptors for texture classification’, Inf. Sci. (Ny), 2018, 467, pp. 634–653.
    32. 32)
      • 22. Longmore, C.A., Liu, C.H., Young, A.W.: ‘The importance of internal facial features in learning new faces’, Q. J. Exp. Psychol., 2015, 68, (2), pp. 249–260.
    33. 33)
      • 38. Ruichek, Y.: ‘Local concave-and-convex micro-structure patterns for texture classification’, Pattern Recognit., 2018, 76, pp. 303–322.
    34. 34)
      • 63. Gaston, J., Ming, J., Crookes, D.: ‘Unconstrained face identification with multi-scale block-based correlation’. 2017 IEEE int. Conf. on acoustics, speech and signal processing (ICASSP), New Orleans, LA, USA, 2017, pp. 1477–1481.
    35. 35)
      • 34. Subrahmanyam, M., Maheshwari, R.P., Balasubramanian, R.: ‘Local maximum edge binary patterns: a new descriptor for image retrieval and object tracking’, Signal Process., 2012, 92, (6), pp. 1467–1479.
    36. 36)
      • 37. Ding, C., Choi, J., Tao, D., et al: ‘Multi-directional multi-level dual-cross patterns for robust face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 38, (3), pp. 518–531.
    37. 37)
      • 52. Lu, J., Zhao, T., Zhang, Y.: ‘Feature selection based-on genetic algorithm for image annotation’, Knowl.-Based Syst., 2008, 21, (8), pp. 887–891.
    38. 38)
      • 45. Wu, J., Xu, Z., Jeon, G., et al: ‘Arithmetic coding for image compression with adaptive weight-context classification’, Signal Process. Image Commun., 2013, 28, (7), pp. 727–735.
    39. 39)
      • 53. Huang, G.B., Mattar, M., Berg, T., et al: ‘Labeled faces in the wild: a database forstudying face recognition in unconstrained environments’, 2008.
    40. 40)
      • 44. Dan, Z., Chen, Y., Yang, Z., et al: ‘An improved local binary pattern for texture classification’, Optik (Stuttg), 2014, 125, (20), pp. 6320–6324.
    41. 41)
      • 62. Eliades, C., Lenc, L., Král, P., et al: ‘Automatic face recognition with well-calibrated confidence measures’, Mach. Learn., 2019, 108, (3), pp. 511–534.
    42. 42)
      • 40. Chahi, A., Ruichek, Y., Touahni, R.: ‘Local directional ternary pattern: a new texture descriptor for texture classification’, Comput. Vis. Image Underst., 2018, 169, pp. 14–27.
    43. 43)
      • 32. Tan, X., Triggs, B.: ‘Enhanced local texture feature sets for face recognition under difficult lighting conditions’, IEEE Trans. Image Process., 2010, 19, (6), pp. 1635–1650.
    44. 44)
      • 4. Turk, M., Pentland, A.: ‘Face recognition using eigenfaces’. Proc. 1991 IEEE computer society Conf. on computer vision and pattern recognition, Maui, HI, USA, 1991, pp. 586–587.
    45. 45)
      • 14. Xu, Z., Jiang, Y., Wang, Y., et al: ‘Local polynomial contrast binary patterns for face recognition’, Neurocomputing, 2019, 355, pp. 1–12.
    46. 46)
      • 23. Rivera, A.R., Castillo, J.R., Chae, O.O.: ‘Local directional number pattern for face analysis: face and expression recognition’, IEEE Trans. Image Process., 2012, 22, (5), pp. 1740–1752.
    47. 47)
      • 25. Pavithra, P., Ganesh, A.B.: ‘Detection of human facial behavioral expression using image processing’, ICTACT J. Image Video Process., 2011, 1, pp. 162–165.
    48. 48)
      • 68. Yee, S.Y., Rassem, T.H., Mohammed, M.F., et al: ‘Face recognition using laplacian completed local ternary pattern (LapCLTP)’, in Zakaria, Z., Ahmad, R. (Eds.): ‘Advances in electronics engineering’ (Springer, Singapore, 2020), pp. 315–327.
    49. 49)
      • 48. Duan, L., Liao, X., Xiang, T.: ‘A secure arithmetic coding based on markov model’, Commun. Nonlinear Sci. Numer. Simul., 2011, 16, (6), pp. 2554–2562.
    50. 50)
      • 51. Yang, S., Liao, X., Borasy, U.: ‘A pedestrian detection method based on the HOG-LBP feature and gentle AdaBoost’, Int. J. Adv. Comput. Technol., 2012, 4, (19), pp. 553–560.
    51. 51)
      • 35. Zhao, Y., Jia, W., Hu, R.-X., et al: ‘Completed robust local binary pattern for texture classification’, Neurocomputing, 2013, 106, pp. 68–76.
    52. 52)
      • 55. Viola, P., Jones, M.J.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 57, (2), pp. 137–154.
    53. 53)
      • 10. Dreuw, P., Steingrube, P., Hanselmann, H., et al: ‘SURF-Face: face recognition under viewpoint consistency constraints’. British Machine Vision Conf., London, UK, 2009, pp. 1–11.
    54. 54)
      • 36. Rajput, S., Bharti, D.J.: ‘A face recognition using linear-diagonal binary graph pattern feature extraction method’, Int. J. Found. Comput. Sci. And Technol. (IJFCST, 2016, 6, (2), pp. 55–65.
    55. 55)
      • 3. Singh, S., Prasad, S.: ‘Techniques and challenges of face recognition: a critical review’, Procedia Comput. Sci., 2018, 143, pp. 536–543.
    56. 56)
      • 26. Zhang, W., Shan, S., Gao, W., et al: ‘Local gabor binary pattern histogram sequence (lgbphs): A novel non-statistical model for face representation and recognition’. Tenth IEEE Int. Conf. on Computer Vision (ICCV'05) Volume 1, Beijing, People's Republic of China, 2005, vol. 1, pp. 786–791.
    57. 57)
      • 24. Aung, M.S.H., Kaltwang, S., Romera-Paredes, B., et al: ‘The automatic detection of chronic pain-related expression: requirements, challenges and the multimodal EmoPain dataset’, IEEE Trans. Affect. Comput., 2015, 7, (4), pp. 435–451.
    58. 58)
      • 66. Wright, J., Yang, A.Y., Ganesh, A., et al: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 31, (2), pp. 210–227.
    59. 59)
      • 13. Jain, A.K., Li, S.Z.: ‘Handbook of face recognition’ (Springer, UK, 2011), vol. 1.
    60. 60)
      • 8. Ahonen, T., Hadid, A., Pietikainen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 2037–2041.
    61. 61)
      • 58. Sanderson, C., Lovell, B. C.: ‘Multi-region probabilistic histograms for robust and scalable identity inference’. Int. Conf. on biometrics, Alghero, Italy, 2009, pp. 199–208.
    62. 62)
      • 43. Pietikäinen, M., Zhao, G.: ‘Two decades of local binary patterns: A survey’, in Bingham, E., Kaski, S., Laaksonen, J., et al (Eds.): ‘Advances in independent component analysis and learning machines’ (Elsevier, UK, 2015), pp. 175–210.
    63. 63)
      • 6. Yan, S., Xu, D., Zhang, B., et al: ‘Graph embedding and extensions: A general framework for dimensionality reduction’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 29, (1), pp. 40–51.
    64. 64)
      • 11. Weng, R., Lu, J., Tan, Y.-P.: ‘Robust point set matching for partial face recognition’, IEEE Trans. Image Process., 2016, 25, (3), pp. 1163–1176.
    65. 65)
      • 59. Guo, Z., Zhang, L., Zhang, D., et al: ‘Hierarchical multi-scale LBP for face and palmprint recognition’. 2010 IEEE Int. Conf. on Image Processing, Hong Kong, Hong Kong, 2010, pp. 4521–4524.
    66. 66)
      • 42. Haindl, M., Filip, J.: ‘Visual texture: accurate material appearance measurement, representation and modeling’ (Springer Science & Business Media, UK, 2013).
    67. 67)
    68. 68)
      • 39. Zeng, H., Chen, J., Cui, X., et al: ‘Quad binary pattern and its application in mean-shift tracking’, Neurocomputing, 2016, 217, pp. 3–10.
    69. 69)
      • 28. Liu, F., Tang, Z., Tang, J.: ‘WLBP: Weber local binary pattern for local image description’, Neurocomputing, 2013, 120, pp. 325–335.
    70. 70)
      • 31. Topi, M., Timo, O., Matti, P., et al: ‘Robust texture classification by subsets of local binary patterns’. Proc. 15th Int. Conf. on Pattern Recognition. ICPR-2000, 2000, vol. 3, pp. 935–938.
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