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A novel high-performance holistic descriptor for face retrieval

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Abstract

Texture extraction-based classification has become the facto methodology applied in face recognition. Haralick feature extraction from gray-level co-occurrence matrix (GLCM) is one of the basic holistic studies that has inspired many face recognition algorithms. This paper presents a theoretically simple, yet efficient, holistic approach that utilizes the spatial relationships of the same pixel patterns occurring at different positions in an image rather than their occurrence statistics as applied in GLCM-based counterparts. The matrix holding the statistical values for the total displacement of the pixel patterns is called the gray-level total displacement matrix (GLTDM). Three approaches are proposed for feature extraction. In the first approach, classical Haralick features extraction is conducted. The second approach (D_GLTDM) utilizes the GLTDM directly as the feature vector rather than extra feature extraction process. In the last approach, principle component analysis (PCA) is used as the feature extraction method. Comprehensive simulations are conducted on images retrieved from the popular face databases, namely face94, ORL, JAFFE and Yale. The performance of the proposed method is compared with that of GLCM, local binary pattern and PCA used in the leading studies. The simulation results and their comparative analysis show that D_GLTDM exhibits promising results and outperforms the other leading methods in terms of classification accuracy.

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References

  1. Lei Z, Liao S, Pietikainen M, Li SZ (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Trans Image Process 20(1):247–256

    MathSciNet  Google Scholar 

  2. Dubey SR (2017) Local directional relation pattern for unconstrained and robust face retrieval. arXiv:1709.09518 [cs.CV]

  3. Eleyan A, Demirel H (2009) Co-occurrence based statistical approach for face recognition. In: 4th international symposium on computer and information sciences, pp 611–615

  4. Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650

    MathSciNet  MATH  Google Scholar 

  5. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 34(4):399–485

    Google Scholar 

  6. Dong Y, Tao D, Li X, Ma J, Pu J (2015) Texture classification and retrieval using shearlets and linear regression. IEEE Trans Cybern 120(3):358–369

    Google Scholar 

  7. Dong Y, Tao D, Li X (2015) Nonnegative multiresolution representation-based texture image classification. ACM Trans Intell Syst Technol 7(3):1–21

    Google Scholar 

  8. Obula Konda Reddy R, Eswara Reddy B, Keshava Reddy E (2014) An effective GLCM and binary pattern schemes based classification for rotation invariant fabric textures. Int J Comput Eng Sci 4(1):1–15

    Google Scholar 

  9. Haralick R, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Google Scholar 

  10. Rivera AR, Castillo R, Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    MathSciNet  MATH  Google Scholar 

  11. Rivera AR, Chae O (2015) Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE Trans Pattern Anal Mach Intell 37(10):2146–2152

    Google Scholar 

  12. Dong Y, Feng J, Liang L, Zheng L, Wu Q (2017) Multiscale sampling based texture image classification. IEEE Signal Process Lett 24(5):614–618

    Google Scholar 

  13. Li Pang Y, Yuan Y (2010) Robust tensor analysis with L1-norm. IEEE Trans Circuits Syst Video Technol 20(2):172–178

    Google Scholar 

  14. Turk MA, Pentland AP (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Google Scholar 

  15. Kirby M, Sirovich L (1990) Applications of the Karhunen–Loeve procedure for the characterisation of human faces. Trans Pattern Anal Mach Intell 12:103–108

    Google Scholar 

  16. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188

    Google Scholar 

  17. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Google Scholar 

  18. Liao S, Zhu X, Lei Z, Zhang L, Li SZ (2007) Learning multi-scale block local binary patterns for face recognition. In: Proceedings of the international conference on advances in biometrics, pp 828–837

  19. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns, application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041

    MATH  Google Scholar 

  20. Ojala T, Pietikainen M, Mienpaa T (2002) Multiresolution grey-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Google Scholar 

  21. Ali A, Jing X, Saleem N (2011) GLCM-based fingerprint recognition algorithm. In: Proceedings of the 4th IEEE international conference on broadband network and multimedia technology (IC-BNMT), pp 207–211

  22. Champion I, Germain C, Da Costa J-P, Alborini A, Dubois-Fernandez P (2014) Retrieval of forest stand age from SAR image texture for varying distance and orientation values of the grey level co-occurrence matrix. IEEE Geosci Remote Sens Lett 11(1):5–9

    Google Scholar 

  23. Vujasinovic T et al (2015) Grey-level co-occurrence matrix texture analysis of breast tumor images in prognosis of distant metastasis risk. Microsc Microanal 21:1–9

    Google Scholar 

  24. Wenbo W et al (2015) Sea ice classification of SAR image based on wavelet transform and grey-level co-occurrence matrix. In: Fifth international conference on instrumentation and measurement, computer, communication and control (IMCCC), pp 104–107

  25. Pratiwi M et al (2015) Mammograms classification using grey-level co-occurrence matrix and radial basis function neural network. Procedia Comput Sci 59(Iccsci):83–91

    Google Scholar 

  26. Ou X, Pan W, Xiao P (2014) In vivo skin capacitive imag-ing analysis by using grey level co-occurrence matrix (GLCM). Int J Pharm 460(1–2):28–32

    Google Scholar 

  27. Adur J, Carvalho HF, Cesar CL (2014) Nonlinear optical microscopy signal processing strategies in cancer. Cancer Inform 13(13):67–76

    Google Scholar 

  28. Ulaby FT et al (1986) Textural information in SAR images. IEEE Trans Geosci Remote Sens GE-24(2):235–245

    Google Scholar 

  29. Eleyan A, Demirel H (2014) Co-occurrence matrix and its statistical features as a new approach for face recognition. Turk J Electr Eng Comput Sci 19:97–107

    Google Scholar 

  30. Nanni L et al (2013) Different approaches for extracting information from the co-occurrence matrix. PLoS ONE 8(12):1–12

    Google Scholar 

  31. Sklansky J (1978) Image segmentation and feature extraction. IEEE Trans Syst Man Cybern SMC-8:237–247

    Google Scholar 

  32. Coggins JM (1982) A framework for texture analysis based on spatial filtering. Ph.D. thesis, Michigan State University, East Lansing, Michigan

  33. Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, 2nd edn. Gatesmark Publishing, Knoxville

    Google Scholar 

  34. Gonzalez RC, Woods RE (2008) Digital image processing, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  35. Sirovich L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. J Opt Soc Am A 4(3):519–524

    Google Scholar 

  36. Kirby M, Sirovich L (1990) Application of the Karhunen–Loeve procedure for the characterization of human faces. IEEE Trans Pattern Anal Mach Intell 12(1):103–108

    Google Scholar 

  37. Turk M, Pentland A (1991) Face recognition using eigenfaces. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–591

  38. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Google Scholar 

  39. Libor S (2000) Face recognition database from University of Essex (UK). http://cmp.felk.cvut.cz/~spacelib/faces/

  40. Sarasota FL (1994) In: Proceedings of the 2nd IEEE workshop on applications of computer vision

  41. Lyons MJ, Akemastu S, Kamachi M, Gyoba J (1998) Coding facial expressions with Gabor wavelets. In: 3rd IEEE international conference on automatic face and gesture recognition, pp 200–205

  42. Viola P, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: IEEE computer society conference on computer vision and pattern recognition

  43. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Google Scholar 

  44. Liao S, Jain AK, Li SZ (2015) A fast and accurate unconstrained face detector. IEEE Trans Pattern Anal Mach Intell TPAMI 38:211–223

    Google Scholar 

  45. Wang Y-Q (2014) An analysis of the Viola-Jones face detection algorithm. Image Process Line 4:128–148

    Google Scholar 

  46. Tan X, Triggs B (2007) Enhanced local texture feature sets for face recognition under difficult lighting conditions. Lect Notes Comput Sci LNCS 4778:168–182

    MATH  Google Scholar 

  47. Chakraborty S, Singh SK, Chakraborty P (2017) Local directional gradient pattern: a local descriptor for face recognition. Multimedia Tools Appl 76:1201–1216

    Google Scholar 

  48. Dan Z, Chen Y, Yang Z, Guang W (2014) An improved local binary pattern for texture classification. Optik 125:6320–6324

    Google Scholar 

  49. Qian X, Hua X-S, Chen P, Ke L (2011) PLBP: an effective local binary patterns texture descriptor with pyramid representation. Pattern Recognit 44:2502–2515

    Google Scholar 

  50. Zhong F, Zhang J (2013) Face recognition with enhanced local directional patterns. Neurocomputing 119:375–384

    Google Scholar 

  51. Liu L, Fieguth P, Guo Y, Wang X, Pietikainen M (2017) Local binary features for texture classification: taxonomy and experimental study. Pattern Recogn 62:135–160

    Google Scholar 

  52. Yang B, Chen S (2013) A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120:365–379

    Google Scholar 

  53. Nanni L, Brahnam S, Ghidoni S, Menegatti E, Barrier T (2013) A comparison of methods for extracting information from the co-occurrence matrix for subcellular classification. Expert Syst Appl 40:7457–7467

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Istanbul Arel University, Istanbul, Turkey

    Nazife Çevik

  2. Department of Software Engineering, Istanbul Aydin University, Istanbul, Turkey

    Taner Çevik

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  1. Nazife Çevik
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  2. Taner Çevik
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Correspondence to Taner Çevik.

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Çevik, N., Çevik, T. A novel high-performance holistic descriptor for face retrieval. Pattern Anal Applic 23, 371–383 (2020). https://doi.org/10.1007/s10044-019-00803-5

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