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A comprehensive comparative study of handcrafted methods for face recognition LBP-like and non LBP operators

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Abstract

Pattern recognition and computer vision fields experienced the proposal of several architectures and approaches to deal with the demands of real world applications including face recognition. They have almost the same structure, based generally on a series of steps where the main ones are feature extraction and classification. The literature works interessted in face recognition problems, insist on the role of texture description as one of the key elements in face analysis, since it greatly affects recognition accuracy. Therefore, texture feature extraction has gained much attention and became a long-standing research topic thanks to its abilities to efficiently understand the face recognition process, especially in terms of face description. Recently, several literature researches in face application proposed new architectures based on pattern description proved by their discriminative power when extracting the feature information from facial images. These advantages combined with an outstanding performance in many classification applications, allowed the LBP-like descriptors to be one of the most prominent texture description method. Given this period of remarkable evolution, this research work includes a comprehensive analytical study of the face recognition performance of 64 LBP-like and 3 non-LBP texture descriptors recently proposed in the literature. To this end, we adopted a face recognition framework composed of four stages: 1) image pre-processing using gamma correction; 2) feature extraction using texture descriptors; 3) histogram calculation and 4) face recognition and classification based on the simple parameter-free Nearest Neighbors classifier (NN). The conducted comprehensive evaluations and experiments on the challenging and widely used benchmarks ORL, YALE, Extended YALE B and FERET databases presenting different challenges, indicate that a number of evaluated texture descriptors, which are tested for the first time on face recognition task, achieve better or competitive compared to several recent systems reported in face recognition literature.

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References

  1. Abdullah M F A, Sayeed M S, Muthu K S, Bashier H K, Azman A, Ibrahim S Z (2014) Face recognition with symmetric local graph structure (slgs). Expert Syst Appl 41(14):6131–6137

    Google Scholar 

  2. Abhishree T M, Latha J, Manikantan K, Ramachandran S (2015) Face recognition using Gabor filter based feature extraction with anisotropic diffusion as a preprocessing technique. Proc Comput Sci 45:312–321

    Google Scholar 

  3. Ahonen T et al (2008) Recognition of blurred faces using local phase quantization. ICPR

  4. Bashier H, Khalifa HOE, Siong L, Hui LT et al (2016) Texture classification via extended local graph structure. Optik-International Journal for Light and Electron Optics 127(2):638–643

    Google Scholar 

  5. Belahcene M, Laid M, Chouchane A, Ouamane A, Bourennane S (2016) Local descriptors and tensor local preserving projection in face recognition. In: 2016 6th European workshop on visual information processing (EUVIP), (pp 1-6). IEEE

  6. Bereta M, Pedrycz W, Reformat M (2013) Local descriptors and similarity measures for frontal face recognition: a comparative analysis. J Visual Commun Image Represent 24(8):1213–1231

    Google Scholar 

  7. Caroline S (2015) Thierry Bouwmans, and Carl Frélicot. An eXtended center-symmetric local binary pattern for background modeling and subtraction in videos. International Joint Conference on Computer Vision Imaging and Computer Graphics Theory and Applications

  8. Cavalcanti G D C, Ren T I, Pereira J F (2013) Weighted modular image principal component analysis for face recognition. Expert Syst Appl 40:4971–4977

    Google Scholar 

  9. Chakraborti T et al (2018) Loop descriptor: Local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635–639

    Google Scholar 

  10. Chakraborty S, Singh SK, Chakraborty P (2017) Local quadruple pattern: A novel descriptor for facial image recognition and retrieval. Computers & Electrical Engineering

  11. Chang C-I, Chen Y (2004) Gradient texture unit coding for texture analysis. Opt Eng 43(8):1891–1903

    Google Scholar 

  12. Dong S, Yang J, Chen Y et al (2015) Finger vein recognition based on multi-orientation weighted symmetric local graph structure. KSII Trans Internet Inf Syst, vol. 9, no 10

  13. Dong S, Yang J, Wang C et al (2005) A new finger vein recognition method based on the difference symmetric local graph structure (DSLGS). Int J Signal Processing Image Process Pattern Recogn 8(10):71–80

    Google Scholar 

  14. Dora L, Agrawal S, Panda R, Abraham A (2017) An evolutionary single Gabor kernel based filter approach to face recognition. Eng Appl Artif Intel 62:286–301

    Google Scholar 

  15. Doshi NP, Schaefer G (2012) A comprehensive benchmark of local binary pattern algorithms for texture retrieval. In: 2012 21st international conference on pattern recognition (ICPR), IEEE

  16. El Aroussi M, El Hassouni M, Ghouzali S, Rziza M, Aboutajdine D (2011) Local appearance based face recognition method using block based steerable pyramid transform. Signal Process 91(1):38–50

    MATH  Google Scholar 

  17. El khadiri I, Chahi A, El merabet Y, Ruichek Y, Touahni R (2018) Local directional ternary pattern: A new texture descriptor for texture classification. Computer Vision and Image Understanding

  18. El khadiri I, Kas M, El merabet Y, Ruichek Y, Touahni R (2018) Repulsive-and-attractive local binary gradient contours: New and efficient feature descriptors for texture classification. Information Sciences

  19. El merabet Y, Ruichek Y (2018) Local Concave-and-Convex Micro-Structure Patterns for texture classification. Pattern Recognit 76:303–322

    Google Scholar 

  20. El Merabet Y, Ruichek Y (2019) Attractive-and-repulsive center-symmetric local binary patterns for texture classification. Eng Appl Artif Intel 78:158–172

    Google Scholar 

  21. Fathi A, Alirezazadeh P, Abdali-Mohammadi F (2016) A new Global-Gabor-Zernike feature descriptor and its application to face recognition. J Vis Commun Image Represent 38:65–72

    Google Scholar 

  22. Fernández A, Álvarez MX, Bianconi F (2011) Image classification with binary gradient contours. Opt Lasers Eng 49(9):1177–1184

    Google Scholar 

  23. Fernández A, Álvarez MX, Bianconi F (2013) Texture description through histograms of equivalent patterns. J Math Imaging Vision 45(1):76–102

    MathSciNet  MATH  Google Scholar 

  24. Fernández A, Marcos XA, Francesco B (2013) Texture description through histograms of equivalent patterns. J Math Imaging Vision 45(1):76–102

    MathSciNet  MATH  Google Scholar 

  25. Fu X, Wei W (2008) Centralized binary patterns embedded with image euclidean distance for facial expression recognition. 4th International Conference on Natural Computation, Vol. 4

  26. Gaidhane VH, Hote YV, Singh V (2014) An efficient approach for face recognition based on common eigenvalues. Pattern Recogn 47(5):1869–1879

    Google Scholar 

  27. Gao T et al (2013) A novel face feature descriptor using adaptively weighted extended LBP pyramid. Optik-International Journal for Light and Electron Optics 124 (23):6286–6291

    Google Scholar 

  28. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Google Scholar 

  29. Ghinea G, Kannan R, Kannaiyan S (2014) Gradient-orientation-based PCA subspace for novel face recognition. IEEE Access 2:914–920

    Google Scholar 

  30. Guan N, Tao D, Luo Z, Yuan B (2012) NeNMF: an optimal gradient method for nonnegative matrix factorization. IEEE Trans Signal Process 60(6):2882–2898

    MathSciNet  MATH  Google Scholar 

  31. Gupta R, Patil H, Mittal A (2010) Robust order-based methods for feature description. 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE

  32. Hadid A et al (2015) Gender and texture classification: A comparative analysis using 13 variants of local binary patterns. Pattern Recogn Lett 68:231–238

    Google Scholar 

  33. He D-C, Wang L (1990) Texture unit, texture spectrum, and texture analysis. IEEE Trans Geosci Remote Sens 28(4):509–512

    Google Scholar 

  34. He DC, Li W (1992) Unsupervised textural classification of images using the texture spectrum. Pattern Recogn 25(3):247–255

    MathSciNet  Google Scholar 

  35. Heikkilä M, Matti P, Cordelia S (2006) Description of interest regions with center-symmetric local binary patterns. Computer vision, graphics and image processing. Springer, Berlin, pp 58–69

    Google Scholar 

  36. Heisele B, Serre T, Poggio T (2007) A component-based framework for face detection and identification. Int J Comput Vis 74(2):167–181

    Google Scholar 

  37. Huang D et al (2011) Local binary patterns and its application to facial image analysis: A survey. IEEE Trans Syst Man Cybern Part C Appl Rev 41(6):765–781

    Google Scholar 

  38. Huang SM, Yang J (2013) Linear discriminant regression classification for face recognition. IEEE Signal Process Lett 20(1):91–94

    Google Scholar 

  39. Huang Z H, Li W J, Shang J, Wang J, Zhang T (2015) Non-uniform patch based face recognition via 2D-DWT. Image Vis Comput 37:12–19

    Google Scholar 

  40. Huanqiang Z et al (2016) Quad binary pattern and its application in mean-shift tracking. Neurocomputing 217:3–10

    Google Scholar 

  41. Hui Z, Dong X, Wang X (2011) Improved center-symmetric local binary pattern descriptor for local feature region description. Energy Procedia 11:1032–1038

    Google Scholar 

  42. Islam MS (2013) Local gray code pattern (LGCP): A robust feature descriptor for facial expression recognition. International Journal of Science and Research (IJSR)

  43. Jabid T, Kabir H Md, Chae O (2010) Local directional pattern (LDP) for face recognition. 2010 Digest of technical papers international conference on consumer electronics (ICCE). IEEE

  44. Jacobs DW, Belhumeur PN, Basri R (1998) Comparing images under variable illumination. Computer Vision and Pattern Recognit

  45. Jiang J, Chen C, Ma J, Wang Z, Wang Z, Hu R (2017) SRLSP: A face image super-resolution algorithm using smooth regression with local structure prior. IEEE Trans Multimed 19(1):27–40

    Google Scholar 

  46. Jin H, Liu Q, Lu H, Tong X (2004) Face detection using improved lbp under bayesian framework. In: 3rd international conference on image and graphics (ICIG 04), Hong Kong, China, 18-20, 306–309

  47. Kaya Y, Ertuğrul ÖF, Tekin R (2015) Two novel local binary pattern descriptors for texture analysis. Appl Soft Comput 34:728–735

    Google Scholar 

  48. Lee K-C, Ho J, Kriegman DJ (2005) Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans Pattern Anal Mach Intell 27(5):684–698

    Google Scholar 

  49. Li H, Suen CY (2016) Robust face recognition based on dynamic rank representation. Pattern Recogn 60:13–24

    Google Scholar 

  50. Li L, Gao J, Ge H (2016) A new face recognition method via semi-discrete decomposition for one sample problem. Optik-International Journal for Light and Electron Optics 127(19):7408–7417

    Google Scholar 

  51. Liu L et al (2017) Local binary features for texture classification: Taxonomy and experimental study. Pattern Recogn 62:135–160

    Google Scholar 

  52. Liu L et al (2017) Local binary features for texture classification: Taxonomy and experimental study. Pattern Recogn 62:135–160

    Google Scholar 

  53. Liu T, Mi J X, Liu Y, Li C (2016) Robust face recognition via sparse boosting representation. Neurocomputing 214:944–957

    Google Scholar 

  54. Lu G F, Zou J, Wang Y (2012) Incremental complete LDA for face recognition. Pattern Recogn 45(7):2510–2521

    MATH  Google Scholar 

  55. Lumini A, Nanni L, Brahnam S (2017) Ensemble of texture descriptors and classifiers for face recognition. Applied Computing and Informatics 13(1):79–91

    Google Scholar 

  56. Madrid CFJ, Carnicer RM, et al. (2003) Simplified texture unit: a new descriptor of the local texture in gray-level images. In: Iberian Conference on Pattern Recognition and Image Analysis, pp 470–477

  57. Mehta R, Egiazarian K (2016) Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recogn Lett 71:16–22

    Google Scholar 

  58. Mehta R, Egiazarian KO (2013) Rotated local binary pattern (RLBP)-rotation invariant texture descriptor. ICPRAM

  59. Muqeet MA, Holambe RS (2017) Local appearance-based face recognition using adaptive directional wavelet transform. Journal of King Saud University-Computer and Information Sciences

  60. Nanni L, Brahnam S, Lumini A (2010) A local approach based on a local binary patterns variant texture descriptor for classifying pain states. Expert Syst Appl 37(12):7888–7894

    Google Scholar 

  61. Nanni L, Lumini A, Brahnam S (2012) Survey on LBP based texture descriptors for image classification. Expert Syst Appl 39(3):3634–3641

    Google Scholar 

  62. Nguyen T P, Vu N-S, Manzanera A (2016) Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173:1565–1577

    Google Scholar 

  63. Ouslimani F, Ouslimani A, Ameur Z (2018) Rotation-invariant features based on directional coding for texture classification. Neural Computing and Applications, 1–8

  64. Pan J, Wang X S, Cheng Y H (2016) Single-sample face recognition based on LPP feature transfer. IEEE Access 4:2873–2884

    Google Scholar 

  65. Phillips P J, Moon H, Rizvi S A, Rauss P J (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22 (10):1090–1104

    Google Scholar 

  66. Piao N, Park R-H (2015) Face recognition using dual difference regression classification. IEEE Signal Process Lett 22(12):2455–2458

    Google Scholar 

  67. Pu H, Guangwei G, Chengshan Q, et al. (2017) Fuzzy linear regression discriminant projection for face recognition. IEEE Access

  68. PVSSR, Mouli C (2016) Dimensionality reduced local directional pattern (DR-LDP) for face recognition. Expert Syst Appl 63:66–73

    Google Scholar 

  69. Rajput S, Bharti J (2016) A face recognition using linear -diagonal binary graph pattern feature extraction method. International Journal in Foundations of Computer Science and Technology (IJFCST) 6(2):118. https://doi.org/10.5121/ijfcst.2016.6205

    Google Scholar 

  70. Ramirez Rivera A, Rojas Castillo J, Oksam Chae O (2013) Local directional number pattern for face analysis: Face and expression recognition. IEEE Trans Image Process 22(5):1740–1752. https://doi.org/10.1109/TIP.2012.2235848

    MathSciNet  MATH  Google Scholar 

  71. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. Applications of Computer Vision, 1994. Proceedings of the Second IEEE Workshop on IEEE

  72. Sayeed M, Yusof I, Bashier H K, Hossen M, Abdullah M F A (2013) Plant identification based on leaf shape and texture pattern using local graph structure (LGS). Aust J Basic Appl Sci 7(11):29–35

    Google Scholar 

  73. Secchi P, Vantini S, Zanini P (2013) Hierarchical independent component analysis: a multi-resolution non-orthogonal data-driven basis. Comput Stat Data Anal 67:236–247

    MATH  Google Scholar 

  74. Shucheng H, Zhuang L (2016) Exponential discriminant locality preserving projection for face recognition. Neurocomputing 208:373–377

    Google Scholar 

  75. Song K, Yan Y, Zhao Y et al (2015) Adjacent evaluation of local binary pattern for texture classification. J Vis Commun Image Represent 33:323–339

    Google Scholar 

  76. Subrahmanyam M, Maheshwari RP, Balasubramanian R (2012) Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Process 92(6):1467–1479

    Google Scholar 

  77. Sun J et al (2014) Concave-convex local binary features for automatic target recognition in infrared imagery. EURASIP Journal on Image and Video Processing 2014(1):1–13

    Google Scholar 

  78. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. International Workshop on Analysis and Modeling of Faces and Gestures

  79. Tian D, Tao D (2016) Coupled learning for facial deblur. IEEE Trans Image Process 25(2):961–972

    MathSciNet  MATH  Google Scholar 

  80. Topi M, Timo O, Matti P, Maricor S (2000) Robust texture classification by subsets of local binary patterns. In: 15th international conference on pattern recognition, 2000. Proceedings (Vol 3, pp 935-938). IEEE

  81. Verma M, Raman B (2017) Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval. Multimedia Tools and Applications, 1–24

  82. Vipparthi SK et al (2015) Local Gabor maximum edge position octal patterns for image retrieval. Neurocomputing 167:336–345

    Google Scholar 

  83. Vipparthi SK, Nagar SK (2016) Local extreme complete trio pattern for multimedia image retrieval system. Int J Autom Comput 13(5):457–467

    Google Scholar 

  84. Wang Y, Tang YY, Li L (2016) Correntropy matching pursuit with application to robust digit and face recognition. IEEE transactions on cybernetics

  85. Wu X, Sun J (2011) An extended center-symmetric local ternary patterns for image retrieval. Advances in Computer Science, Environment, Ecoinformatics, and Education, 359–364

  86. Xu B, Gong P, Seto E, Spear R (2003) Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic IKONOS image. Photogramm Eng Remote Sens 69(5):529–536

    Google Scholar 

  87. Yang W, Wang Z, Zhang B (2016) Face recognition using adaptive local ternary patterns method. Neurocomputing 213:183–190

    Google Scholar 

  88. Yuan S, Mao X, Chen L (2017) Multilinear spatial discriminant analysis for dimensionality reduction. IEEE Trans Image Process 26(6):2669–2681

    MathSciNet  MATH  Google Scholar 

  89. Zhang B et al (2010) Directional binary code with application to PolyU near-infrared face database. Pattern Recogn Lett 31(14):2337–2344

    Google Scholar 

  90. Zhang F, Yang J, Qian J, Xu Y (2015) Nuclear norm-based 2-DPCA for extracting features from images. IEEE transactions on neural networks and learning systems 26(10):2247–2260

    MathSciNet  Google Scholar 

  91. Zhao Y, Huang D-S, Jia W (2012) Completed local binary count for rotation invariant texture classification. IEEE Trans Image Process 21(10):4492–4497

    MathSciNet  MATH  Google Scholar 

  92. Zhao Y, Jia W, Hu R X, et al. (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:68–76

    Google Scholar 

  93. Zhao Y, Wang RG, Wang WM, Gao W (2016) Local quantization code histogram for texture classification. Neurocomputing 207:354–364

    Google Scholar 

  94. Zhou N, Constantinides A G, Huang G, Zhang S (2017) Face recognition based on an improved center symmetric local binary pattern. Neural Comput Applic 2017:1–7

    Google Scholar 

  95. Zhou S-R, Yin J-P, Zhang J-M (2012) LPQ and LBP based Gabor filter for face representation. Neurocomputing

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Acknowledgments

The authors gratefully acknowledge the Scholarship funding received from Centre National de la Recherche Scientifique et Technique (CNRST-Maroc ) under the grant number 7UIT2017.

All the methods source codes are available upon email requests to the corresponding author

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Authors and Affiliations

  1. aboratoire LASTID, Département de Physique, Faculté des Sciences, Université Ibn Tofail, BP 133, 14000, Kenitra, Marocco

    M. Kas, Y. El-merabet & R. Messoussi

  2. CIAD UMR 7533, Université Bourgogne Franche-Comté, UTBM, F-90010, Belfort, France

    M. Kas & Y. Ruichek

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  1. M. Kas
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Kas, M., El-merabet, Y., Ruichek, Y. et al. A comprehensive comparative study of handcrafted methods for face recognition LBP-like and non LBP operators. Multimed Tools Appl 79, 375–413 (2020). https://doi.org/10.1007/s11042-019-08049-3

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