Several human being traits can be used as a robust and distinctive identifier for a given person. The palm region of the hand is one of these features that researchers in biometric fields have given a huge consideration in recent years. Many works have been proposed in the literature to design palmprint (an image acquired of the palm region) recognition framework. Extraction of prominent image local features is a critical module in most of these approaches. Local Binary Patterns (LBP) like methods, have emerged as one of the most effective feature extraction techniques. Despite a period of remarkable evolution, neither extensive and comprehensive evaluation nor comparison has been performed to date on a large number of LBP variants and non-LBP texture methods in palmprint recognition problem. Motivated by this, this paper aims to fill that gap and provide a comprehensive comparative study of the performance of a large number of recent texture descriptors in palmprint recognition. Extensive experimental results on the well-known constrained and unconstrained challenging palmprint databases, indicate that a number of tested local texture descriptors, which are evaluated for the first time on palmprint recognition, achieve promising results. Classification results are statistically compared through Wilcoxon signed rank test.

References

1. 1)
  - 2. Jain, A.K., Ross, A., Nandakumar, K.: ‘Introduction to biometrics’ (Springer, USA, 2011).
2. 2)
  - 32. Guo, Z., Zhang, L., Zhang, D., et al: ‘Hierarchical multiscale LBP for face and palmprint recognition’. Proc. 2010 IEEE, 17th Int. Conf. on Image Processing, Hong Kong, 2010.
3. 3)
  - 61. Madrid-Cuevas, F.J., Medina, R., Prieto, M., et al: ‘Simplified texture unit: a new descriptor of the local texture in gray-level images’. Proc. First Iberian Conf. on Pattern Recognition and Image Analysis (IbPRIA 2003), Berlin, Germany, 2003, vol. 2652, pp. 470–477.
4. 4)
  - 82. 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., 2018, 467, pp. 634–653.
5. 5)
  - 3. Fei, L., Zhang, B., Zhang, W., et al: ‘Local apparent and latent direction extraction for palmprint recognition’, Inf. Sci., 2019, 473, pp. 59–72.
6. 6)
  - 29. Alhakeem, Z., Jang, S.I.: ‘A convolution-free LBP-HOG descriptor for mammogram classification’, arXiv preprint arXiv:190400187, 2019.
7. 7)
  - 72. Kaya, Y., Ertugrul, O.F., Tekin, R.: ‘Two novel local binary pattern descriptors for texture analysis’, Appl. Soft Comput., 2015, 34, pp. 728–735.
8. 8)
  - 33. Mu, M., Ruan, Q., Guo, S.: ‘Shift and grayscale invariant features for palmprint identification using complex directional wavelet and local binary pattern’, Neurocomputing, 2011, 74, pp. 3351–3360.
9. 9)
  - 1. Anil, K.J., Karthik, N., Arun, R.: ‘50 years of biometric research: accomplishments, challenges, and opportunities’, Pattern Recognit. Lett., 2016, 79, pp. 80–105.
10. 10)
  - 87. Zhang, L., Li, L., Yang, A., et al: ‘Towards contactless palmprint recognition: a novel device, a new benchmark, and a collaborative representation based identification approach’, Pattern Recognit., 2017, 46, pp. 4179–4188.
11. 11)
  - 38. Doshi, N.P., Schaefer, G.: ‘A comprehensive benchmark of local binary pattern algorithms for texture retrieval’. 21st Int. Conf. on Pattern Recognition (ICPR), Tsukuba, Japan, 2012, pp. 2760–2763.
12. 12)
  - 14. Pan, X., Ruan, Q., Wang, Y.: ‘Palmprint recognition using Gabor local relative features’, Comput. Eng. Appl., 2012, 48, pp. 706–712.
13. 13)
  - 81. Chakraborty, S., Singh, S.K., Chakraborty, P.: ‘Local quadruple pattern: a novel descriptor for facial image recognition and retrieval’, Comput. Electr. Eng., 2017, 62, pp. 92–104.
14. 14)
  - 79. Faraji, M.R., Qi, X.: ‘Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns’, Neurocomputing, 2016, 199, pp. 16–30.
15. 15)
  - 44. Abdullah, M.F.A., Sayeed, M.S., Muthu, K.S., et al: ‘Face recognition with symmetric local graph structure’, Expert Syst. Appl., 2014, 41, (14), pp. 6131–6137.
16. 16)
  - 10. Xu, Y., Fei, L., Zhang, D.: ‘Combining left and right palmprint images for more accurate personal identification’, IEEE Trans. Image Process., 2015, 24, (2), pp. 549–559.
17. 17)
  - 54. 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., 2016, 38, (3), pp. 518–531.
18. 18)
  - 83. Chakraborty, S., Singh, S.K., Chakraborty, P.: ‘Centre symmetric quadruple pattern: A novel descriptor for facial image recognition and retrieval’, Pattern Recognit. Lett., 2018, 115, pp. 50–58.
19. 19)
  - 36. Huang, D., Shan, C., Ardabilian, M., et al: ‘Local binary patterns and its application to facial image analysis: a survey’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2011, 41, (6), pp. 765–781.
20. 20)
  - 35. Shams, G., Ismail, M., Bassiouny, S., et al: ‘Face and palmprint recognition using hierarchical multiscale adaptive LBP with directional statistical features’. Int. Conf. on Image Analysis and Recognition (ICIAR 2014), Vilamoura, Portugal, 2014, Part II (LNCS, 8815), pp. 102–111.
21. 21)
  - 50. Fernández, A., Alvarez, M.X., Bianconi, F.: ‘Image classification with binary gradient contours’, Opt. Lasers Eng., 2011, 49, (9–10), pp. 1177–1184.
22. 22)
  - 85. 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.
23. 23)
  - 58. Elmerabet, Y., Ruichek, Y., El Idrissi, A.: ‘Attractive-and-repulsive center-symmetric local binary patterns for texture classification’, Eng. Appl. Artif. Intell., 2019, 78, pp. 158–172.
24. 24)
  - 64. Guo, Z., Zhang, L., Zhang, D.: ‘A completed modeling of local binary pattern operator for texture classification’, IEEE Trans. Image Process., 2010, 19, (6), pp. 1657–1663.
25. 25)
  - 49. Kannala, J., Rahtu, E.: ‘BSIF: binarized statistical image features’. 21st Int. Conf. on Pattern Recognition, Tsukuba, Japan, 2012, pp. 1363–1366.
26. 26)
  - 57. El-Khadiri, I., Chahi, A., El Merabet, Y., et al: ‘Local directional ternary pattern: a new texture descriptor for texture classification’, Comput. Vis. Image Underst., 2018, 169, pp. 14–27.
27. 27)
  - 71. Hafiane, A., Palaniappan, K., Seetharaman, G.: ‘Joint adaptive median binary patterns for texture classification’, Pattern Recognit., 2015, 48, (8), pp. 2609–2620.
28. 28)
  - 63. Nanni, L., Brahnam, S., Lumini, A.: ‘A local approach based on a local binary patterns variant texture descriptor for classifying pain states’, Expert Syst. Appl., 2010, 37, (12), pp. 7888–7894.
29. 29)
  - 48. Song, T., Li, H., Meng, F., et al: ‘LETRIST: locally encoded transform feature histogram for rotation-invariant texture classification’, IEEE Trans. Circuits Syst. Video Technol., 2018, 28, (7), pp. 1565–1579.
30. 30)
  - 13. Raghavendra, R., Dorizzi, B., Rao, A., et al: ‘Designing efficient fusion schemes for multimodal biometric systems using face and palmprint’, Pattern Recognit., 2011, 44, pp. 1076–1088.
31. 31)
  - 76. Dubey, S.R., Singh, S.K., Singh, R.K.: ‘Local wavelet pattern: a new feature descriptor for image retrieval in medical CT databases’, IEEE Trans. Image Process., 2015, 24, (12), pp. 5892–5903.
32. 32)
  - 73. Silva, C., Bouwmans, T., Frélicot, C.: ‘An extended center-symmetric local binary pattern for background modeling and subtraction in videos’. Int. Joint Conf. on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, Berlin, Germany, 2015.
33. 33)
  - 80. Verma, M., Raman, B.: ‘Local neighborhood difference pattern: a new feature descriptor for natural and texture image retrieval’, Multimedia Tools Appl., 2018, 77, (10), pp. 11843–11866.
34. 34)
  - 17. Genovese, A., Piuri, V., Plataniotis, K., et al: ‘PalmNet: Gabor-PCA convolutional networks for touchless palmprint recognition’, IEEE Trans. Inf. Forensics Sec., 2019, 14, pp. 3160–3174.
35. 35)
  - 55. Elmerabet, Y., Ruichek, Y.: ‘Local concave-and-convex micro-structure patterns for texture classification’, Pattern Recognit., 2018, 76, pp. 303–322.
36. 36)
  - 77. Rivera, A.R., Castillo, J.R., Chae, O.: ‘Local directional texture pattern image descriptor’, Pattern Recognit. Lett., 2015, 51, pp. 94–100.
37. 37)
  - 67. Fathi, A., Naghsh-Nilchi, A.R.: ‘Noise tolerant local binary pattern operator for efficient texture analysis’, Pattern Recognit. Lett., 2012, 33, (9), pp. 1093–1100.
38. 38)
  - 22. Rida, I., Herault, R., Marcialis, G.L., et al: ‘Palmprint recognition with an efficient data driven ensemble classifier’, Pattern Recognit. Lett., 2019, 126, pp. 21–30.
39. 39)
  - 5. Aoyama, S., Ito, K., Aoki, T.: ‘A finger-knuckle-print recognition algorithm using phase-based local block matching’, Inf. Sci., 2014, 268, pp. 53–64.
40. 40)
  - 20. Hu, D., Feng, G., Zhou, Z.: ‘Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition’, Pattern Recognit., 2007, 40, (1), pp. 339–342.
41. 41)
  - 56. Chakraborti, T., McCane, B., Mills, S., et al: ‘Loop descriptor: local optimal-oriented pattern’, IEEE Signal Process. Lett., 2018, 25, (5), pp. 635–639.
42. 42)
  - 23. Jing, X.Y., Zhang, D.: ‘A face and palmprint recognition approach based on discriminant DCT feature extraction’, IEEE Trans. Syst. Man B, Cybern., 2004, 34, (6), pp. 2405–2415, pp. 1–9.
43. 43)
  - 68. Liu, L., Zhao, L., Long, Y., et al: ‘Extended local binary patterns for texture classification’, Image Vis. Comput., 2012, 30, (2), pp. 86–99.
44. 44)
  - 8. Wu, X., Zhang, D., Wang, K., et al: ‘Palmprint classification using principal lines’, Pattern Recognit., 2004, 37, (10), pp. 1987–1998.
45. 45)
  - 39. Nanni, L., Lumini, A., Brahnam, S.: ‘Survey on LBP based texture descriptors for image classification’, Expert Syst. Appl., 2012, 39, (3), pp. 3634–3641.
46. 46)
  - 25. Chen, G.Y., Xie, W.F.: ‘Pattern recognition with SVM and dual-tree complex wavelets’, Image Vis. Comput., 2007, 25, (6), pp. 960–966.
47. 47)
  - 74. Vipparthi, S.K., Murala, S., Nagar, S.K., et al: ‘Local Gabor maximum edge position octal patterns for image retrieval,’, Neurocomputing, 2015, 167, pp. 336–345.
48. 48)
  - 51. Song, K., Yan, Y., Zhao, Y., et al: ‘Adjacent evaluation of local binary pattern for texture classification’, J. Vis. Commun. Image Represent., 2015, 33, pp. 323–339.
49. 49)
  - 75. Zhu, Z., You, X., Chen, C.P., et al: ‘An adaptive hybrid pattern for noise-robust texture analysis’, Pattern Recognit., 2015, 48, (8), pp. 2592–2608.
50. 50)
  - 31. Hanmandlu, M., Gureja, A., Jain, A.: ‘Palm print recognition using local binary pattern operator and support vector machines’. Int. Conf. on Signal and Image Processing, Chennai, China, 2010.
51. 51)
  - 65. He, Y., Sang, N.: ‘Robust illumination invariant texture classification using gradient local binary patterns’. Int. Workshop on Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), Xiamen, China, 2011, pp. 1–6.
52. 52)
  - 45. Abusham, E.E., Bashir, H.K.: ‘Face recognition using local graph structure (LGS)’. Int. Conf. on Human–Computer Interaction, Berlin, Germany, 2011, pp. 169–175.
53. 53)
  - 53. Mehta, R., Egiazarian, K.: ‘Dominant rotated local binary patterns (DRLBP) for texture classification’, Pattern Recognit. Lett., 2016, 71, pp. 16–22.
54. 54)
  - 30. El-Tarhouni, W., Larbi, B., Mosa, E., et al: ‘Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling’, Neural Comput. Appl., 2019, 31, (2), pp. 593–603.
55. 55)
  - 16. Gumaei, A., Sammouda, R., Al-Salman, A.M.S., et al: ‘Anti-spoofing cloud-based multi-spectral biometric identification system for enterprise security and privacy-preservation’, J. Parallel Distrib. Comput., 2019, 124, pp. 27–40.
56. 56)
  - 26. Hong, D., Liu, W., Su, J., et al: ‘A novel hierarchical approach for multispectral palmprint recognition’, Neurocomputing, 2015, 151, pp. 511–521.
57. 57)
  - 69. Rivera, A.R., Castillo, J.R., Chae, O.O.: ‘Local directional number pattern for face analysis: face and expression recognition’, IEEE Trans. Image Process., 2013, 22, (5), pp. 1740–1752.
58. 58)
  - 62. Ahonen, T., Rahtu, E., Ojansivu, V., et al: ‘Recognition of blurred faces using local phase quantization’. 19th Int. Conf. on Pattern Recognition ICPR, Florida, USA, 2008, pp. 1–4.
59. 59)
  - 15. Gumaei, A., Sammouda, R., Al-Salman, A.M.S., et al: ‘An improved multispectral palmprint recognition system using autoencoder with regularized extreme learning machine’, Comput. Intell. Neurosci., 2018, 2018, pp. 1–13.
60. 60)
  - 86. Zhang, D.D., Kong, W., You, J., et al: ‘Online palmprint identification’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (9), pp. 1041–1050.
61. 61)
  - 34. Zhao, Y., Jia, W., Hu, R., et al: ‘Palmprint identification using LBP and different representations’. Int. Conf. on Hand-Based Biometrics (ICHB), Hong Kong, 2011, pp. 1–5.
62. 62)
  - 37. Fernández, A., Alvarez, M.X., Bianconi, F.: ‘Texture description through histograms of equivalent patterns’, J. Math. Imaging Vis., 2013, 45, pp. 76–102.
63. 63)
  - 52. Bashier, H.K., Hoe, L.S., Hui, L.T., et al: ‘Texture classification via extended local graph structure’, Optik, 2016, 127, pp. 638–643.
64. 64)
  - 43. Ojala, T., Pietikäinen, M., Harwood, D.: ‘A comparative study of texture measures with classification based on featured distributions’, Pattern Recognit., 1996, 29, (1), pp. 51–59.
65. 65)
  - 28. Kong, A.W.K., Zhang, D.: ‘Competitive coding scheme for palmprint verification’. Proc. 17th Int. Conf. on Pattern Recognition, 2004. ICPR 2004, Cambridge, London, 2004, pp. 520–523.
66. 66)
  - 42. Hassaballah, M., Alshazly, H.A., Ali, A.A.: ‘Ear recognition using local binary patterns: a comparative experimental study’, Expert Syst. Appl., 2019, 118, pp. 182–200.
67. 67)
  - 27. Gumaei, A., Sammouda, R., Al-Salman, A., et al: ‘An effective palmprint recognition approach for visible and multispectral sensor images’, Sensors, 2018, 18, (5), p. 1575.
68. 68)
  - 40. Hadid, A., Ylioinas, J., Bengherabi, M., et al: ‘Gender and texture classification: a comparative analysis using 13 variants of local binary patterns’, Pattern Recognit. Lett., 2015, 68, pp. 231–238.
69. 69)
  - 11. Gan, J.Y., Zhou, D.P.: ‘A novel method for palmprint recognition based on wavelet transform’. Proc. 8th IEEE Int. Conf. on Signal Processing, Guilin, China, 2006, vol. 3, pp. 16–20.
70. 70)
  - 66. 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.
71. 71)
  - 4. Umer, S., Dhara, B.C., Chanda, B.: ‘A novel cancelable iris recognition system based on feature learning techniques’, Inf. Sci., 2017, 406, pp. 102–118.
72. 72)
  - 7. Kong, A., Zhang, D., Kamel, M.: ‘A survey of palm print recognition’, Pattern Recognit., 2009, 42, pp. 1408–1418.
73. 73)
  - 19. Yang, J., Frangi, A.F., Yang, J.Y., et al: ‘KPCA plus LDA: a complete kernel fisher discriminant framework for feature extraction and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (2), pp. 230–244.
74. 74)
  - 21. Hu, R.X., Jia, W., Huang, D.S., et al: ‘Maximum margin criterion with tensor representation’, Neurocomputing, 2010, 73, (10), pp. 1541–1549.
75. 75)
  - 24. Kumar, A., Zhang, D.: ‘Personal recognition using hand shape and texture’, IEEE Trans. Image Process., 2006, 15, (8), pp. 2454–2461.
76. 76)
  - 70. Vipparthi, S.K., Nagar, S.K.: ‘Local extreme complete trio pattern for multimedia image retrieval system’, Int. J. Autom. Comput., 2016, 13, (5), pp. 457–467.
77. 77)
  - 59. Patel, D., Stonham, T.: ‘A single layer neural network for texture discrimination’. IEEE Int. Symp. on Circuits and Systems, Singapore, 1991, pp. 2656–2660.
78. 78)
  - 18. Lu, G., Zhang, D., Wang, K.: ‘Palmprint recognition using eigenpalms features’, Pattern Recognit. Lett., 2003, 24, (9–10), pp. 1463–1467.
79. 79)
  - 6. Guo, X., Zhou, W., Zhang, Y.: ‘Collaborative representation with HM-LBP features for palmprint recognition’, Mach. Vis. Appl., 2017, 28, (3–4), pp. 283–291.
80. 80)
  - 41. Liu, L., Fieguth, P., Guo, Y., et al: ‘Local binary features for texture classification: taxonomy and experimental study’, Pattern Recognit., 2017, 62, pp. 135–160.
81. 81)
  - 12. Badrinath, G., Kachhi, N., Gupta, P.: ‘Verification system robust to occlusion using low-order Zernike moments of palmprint sub-images’, Telecommun. Syst., 2010, 47, pp. 275–290.
82. 82)
  - 46. Kas, M., El-Merabet, Y., Ruichek, Y., et al: ‘Mixed neighborhood topology cross decoded patterns for image-based face recognition’, Expert Syst. Appl., 2018, 114, pp. 119–142.
83. 83)
  - 60. Xu, B., Gong, P., Seto, E., et al: ‘Comparison of gray-level reduction and different texture spectrum encoding methods for land-use classification using a panchromatic Ikonos image’, Photogramm. Eng. Remote Sens., 2003, 69, (5), pp. 529–536.
84. 84)
  - 84. Ouslimani, F., Ouslimani, A., Ameur, Z.: ‘Rotation-invariant features based on directional coding for texture classification’, Neural Comput. Appl., 2019, 31, pp. 6393–6400.
85. 85)
  - 47. Faraji, M.R., Qi, X.: ‘Face recognition under varying illumination based on adaptive homomorphic eight local directional patterns’, IET Comput. Vis., 2014, 9, (3), pp. 390–399.
86. 86)
  - 9. Huang, D.S., Jia, W., Zhang, D.: ‘Palmprint verification based on principal lines’, Pattern Recognit., 2008, 41, (4), pp. 1316–1328.
87. 87)
  - 78. Zeng, H., Chen, J., Cui, X., et al: ‘Binary pattern and its application in mean-shift tracking’, Neurocomputing, 2016, 217, pp. 3–10.

Palmprint recognition using state-of-the-art local texture descriptors: a comparative study

References

Related content