Advertisement

RIMFRA: Rotation-invariant multi-spectral facial recognition approach by using orthogonal polynomials

  • Taner CevikEmail author
  • Nazife Cevik
Article
  • 10 Downloads

Abstract

This paper proposes a novel rotation-invariant multi-spectral facial recognition approach (RIMFRA) by using orthogonal polynomials. In the first step, a rotation, illumination and noise invariant local descriptor (RinLd) is proposed to represent the texture patterns of a face image. Color channels of the images embodies non-trivial information about the characteristic of the image. Hence, the local descriptor matrices are extracted among the color channels. The corresponding new descriptor matrices for the red, green and blue channels of the image are extracted. Afterwards, co-occurrence matrices are obtained from the six combinations of the corresponding color channel descriptor matrices, that are red-red, blue-blue, green-green, red-blue, green-blue and red-green. Finally, these matrices are decomposed by using the orthogonal polynomials to achieve a more reliable and characteristic pattern extraction. The coefficients obtained as a result of the decomposition process are used as the ultimate features for the classification of the images. Extensive simulations are conducted over benchmark datasets. As presented by the simulation results, the ultimate features yield very high discriminating performance as well as providing resistance to rotation and illumination variations.

Keywords

Facial recognition rotation invariant multi-spectral orthogonal polynomial 

Notes

References

  1. 1.
    Abutaleb AS (1989) Automatic Thresholding of Gray-level Pictures Using Two-dimensional Entropies. Computer Vision Graphics Image Processing 47:22–32Google Scholar
  2. 2.
    Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041zbMATHGoogle Scholar
  3. 3.
    Allam S, Adel M, Refregier P (1997) Fast Algorithm for Texture Discrimination by Use of a Separable Orthonormal Decomposition of the Co-occurrence Matrix. Appl Opt 36:8313–8321Google Scholar
  4. 4.
    An Approach to Textile Recognition, Pattern Recognition, Peng-Yeng Yin (Ed.), ISBN: 978–953–307-014-8, InTech, Available from: http://www.intechopen.com/books/pattern-recognition/anapproach-to-textile-recognition.
  5. 5.
    Andreu Y, García-Sevilla P, Mollineda RA (2014) Face gender classification: a statistical study when neutral and distorted faces are combined for training and testing purposes. Image Vis Comput 32(1):27–36Google Scholar
  6. 6.
    Arvis V, Debain C, Berducat M, Benassi A (2004) Generalization of the Co-occurrence Matrix for Color Images: Application to Color Texture Classification. Image Analysis and Stereology 23:63–72zbMATHGoogle Scholar
  7. 7.
    Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisher-faces: Recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720Google Scholar
  8. 8.
    Byungyong R, Rivera AR, Kim J, Chae O (2017) Local Directional Ternary Pattern for Facial Expression Recognition. IEEE Trans Image Process 26(12):6006–6018MathSciNetGoogle Scholar
  9. 9.
    Cheong M, Loke KS (2008) Textile Recognition Using Tchebichef Moments of Co-occurrence Matrices. In: Huang DS., Wunsch D.C., Levine D.S., Jo KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Theoretical and Methodological Issues. ICIC. Lecture Notes in Computer Science, vol. 5226. Springer, Berlin, HeidelbergGoogle Scholar
  10. 10.
    Comon P (1994) Independent component analysis - a new concept? Signal Process 36:287–314zbMATHGoogle Scholar
  11. 11.
    Dahmane M, Meunier J (2011) Emotion recognition using dynamic gridbased HoG features. IEEE Int Conf Autom Face Gesture Recognit Workshops (FG):884–888Google Scholar
  12. 12.
    Dan Z, Chen Y, Yang Z, Wu G (2014) An improved local binary pattern for texture classification. Optik 125:6320–6324Google Scholar
  13. 13.
    Davis LS (1981) Image Texture Analysis Techniques - A Survey. In: Simon JC, Haralick RM (eds) Digital Image Processing. D. Reidel, DordrechtGoogle Scholar
  14. 14.
    Dubey SR (2017) Local Directional Relation Pattern for Unconstrained and Robust Face Retrieval. arXiv:1709.09518 [cs.CV]Google Scholar
  15. 15.
    Eskandari M, Toygar O, Demirel H (2014) Feature extractor selection for face-iris multimodal recognition. Signal Image Video Process 8(6):1189–1198Google Scholar
  16. 16.
    Face Recognition Data, University of Essex, UK, Face 94, http://cswww.essex.ac.uk/mv/all faces/faces94.html.
  17. 17.
    Gao W, Cao B, Shan S, Chen X, Zhou D, Zhang X, Zhao D (2008) The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations. IEEE Trans on System Man, and Cybernetics (Part A) 38(1):149–161Google Scholar
  18. 18.
    Hadid A, Dugelay JL, Pietikäinen M (2011) On the use of dynamic features in face biometrics: recent advances and challenges. Signal Image Video Processing 5(4):495–506Google Scholar
  19. 19.
    Hahn F, Sanchez S (2000) Carrot volume evaluation using imaging algorithms. J Agric Eng Res 75:243–249Google Scholar
  20. 20.
    Haralick RM (1979) Statistical and structural approach to texture. Proc IEEE 67(5):786–804Google Scholar
  21. 21.
    Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3:610–621Google Scholar
  22. 22.
    He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. IEEE Int Conf Comput Vis:1208–1213Google Scholar
  23. 23.
    He X, Yan S, Hu Y, Niyogi P, Zhang H (2005) Face recognition using laplacian faces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340Google Scholar
  24. 24.
    Ishikawa Y, Hirata T (2001) Color change model forbroccoli packaged in polymeric films. Transactions of the ASAE 44:923–927Google Scholar
  25. 25.
    Jabid T, Kabir MH, Chae O (2010) Robust facial expression recognition based on local directional pattern. ETRI J 32(5):784–794Google Scholar
  26. 26.
    Jafri R, Arabnia HR (2009) A Survey of Face Recognition Techniques. Journal of Information Processing Systems 5(2):41–68Google Scholar
  27. 27.
    Jain A, Hong L, Pankanti S (2000) Biometric Identification. Commun ACM 43(2):91–98Google Scholar
  28. 28.
    Jain AK, Ross A (2008) Introduction to Biometrics. In: Jain, AK; Flynn; Ross, A. Handbook of Biometrics. Springer. pp. 1–22, ISBN 978–0–387-71040-2Google Scholar
  29. 29.
    Jian M, Lam KM (2013) Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition. Pattern Recogn 46(11):3091–3102Google Scholar
  30. 30.
    Jian M, Lam KM (2014) Face-Image Retrieval Based on Singular Values and Potential-Field Representation. Signal Process 100:9–15Google Scholar
  31. 31.
    Jian M, Lam KM, Dong J (2014) Facial-Feature Detection and Localization Based on a Hierarchical Scheme. Inf Sci 262:1–14Google Scholar
  32. 32.
    Jian M, Lam KM, Dong J (2014) Illumination-insensitive Texture Discrimination Based on Illumination Compensation and Enhancement. Inf Sci 269:60–72MathSciNetGoogle Scholar
  33. 33.
    Jian M, Lam KM, Dong J, Zang W (2018) Comprehensive Assessment of Non-Uniform Illumination for 3D Heightmap Reconstruction in Outdoor Environments. Comput Ind 99:110–118Google Scholar
  34. 34.
    Kaya Y, Ertugrul OF (2017) Gender classification from facial images using gray relational analysis with novel local binary pattern descriptors. Signal Image and Video Processing 11:769–776Google Scholar
  35. 35.
    Khojastehnazhand M, Omid M, Tabatabaeefar A (2009) Determination of orange volume and surface area using image processing technique. International Agrophysics 23:237–242Google Scholar
  36. 36.
    Kim K, Jeong S, Chun BT, Lee JY, Bae Y (1999) Efficient Video Images Retrieval by Using Local Co-occurrence Matrix Texture Features and Normalised Correlation. Proceedings of The IEEE Region 10 Conf 2:934–937Google Scholar
  37. 37.
    Koc AB (2007) Determination of watermelon volume using ellipsoid approximation and image processing. Postharvest Biol Technol 45(3):366–371Google Scholar
  38. 38.
    Krylov AS, Kutovoi AV (2002) Texture Parameterization with Hermite Functions. International Conference Graphicon, Nizhny NovgorodGoogle Scholar
  39. 39.
    Lei Z, Liao S, Pietikäinen M, Li SZ (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Trans Image Process 20(1):247–256MathSciNetGoogle Scholar
  40. 40.
    Li B, Lian XC, Lu BL (2012) Gender classification by combining clothing, hair and facial component classifiers. Neurocomputing 76(1):18–27Google Scholar
  41. 41.
    Liu L, Fieguth P, Guo Y, Wang X, Pietikainen M (2017) Local Binary Features for Texture Classification: Taxonomy and experimental study. Pattern Recogn 62:135–160Google Scholar
  42. 42.
    Lyons MJ, Akamatsu S, Kamachi M, Gyoba J (1998) Coding Facial Expressions with Gabor Wavelets. 3rd IEEE International Conference on Automatic Face and Gesture Recognition, NaraGoogle Scholar
  43. 43.
    Melendez J, Garcia MA, Puig D (2008) Efficient distance-based per-pixel texture classification with Gabor wavelet filters. Pattern Anal Applic 11(3):365–372MathSciNetGoogle Scholar
  44. 44.
    Murala S, Maheshwari RP, Balasubramanian R (2012) Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval. IEEE Trans Image Process 21(5):2874–2886MathSciNetzbMATHGoogle Scholar
  45. 45.
    Nambi VE, Thangavel K, Rajeswari KA, Manickavasagan A, Geetha V (2016) Texture and rheological changes of Indian mango cultivars during ripening. Postharvest Biol Technol 117:152–160Google Scholar
  46. 46.
    Nambi VE, Thangavel K, Shahir S, Thirupathi V (2016) Comparison of various RGB image features for nondestructive prediction of ripening quality of alphonso mangoes for easy adoptability in machine vision applications: a multivariate approach. J Food Qual 39:816–825Google Scholar
  47. 47.
    Nanni L, Brahnam S, Ghidoni S, Menegatti E, Barrier T (2013) Different Approaches for Extracting Information from the Co-Occurrence Matrix. PLoS One 8(12):1–9Google Scholar
  48. 48.
    Nisenson M, Yariv I, El-Yaniv R, Meir R (2003) Towards Behaviometric Security Systems: Learning to Identify a Typist. Lect Notes Comput Sci:363–374Google Scholar
  49. 49.
    Quevedo R, Aguilera J, Pedreschi F (2010) Color of salmon fillets by computer vision and sensory panel. Food Bioprocess Technol:637–643Google Scholar
  50. 50.
    Rai P, Khanna P (2014) A gender classification system robust to occlusion using Gabor features based (2D) PCA. J Vis Commun Image Represent 25(5):1118–1129Google Scholar
  51. 51.
    Rivera AR, Castillo JR, Chae O (2012) Local Directional Number Pattern for Face Analysis: Face and Expression Recognition. IEEE Trans Image Process 22(5):1740–1752MathSciNetzbMATHGoogle Scholar
  52. 52.
    Rivera AR, Castillo R, Chae O (2013) Local directional number pattern for face analysis: Face and expression recognition. IEEE Trans Image Process 22(5):1740–1752MathSciNetzbMATHGoogle Scholar
  53. 53.
    Rivera AR, Chae O (2015) Spatiotemporal directional number transitional graph for dynamic texture recognition. IEEE Trans Pattern Anal Mach Intell 37(10):2146–2152Google Scholar
  54. 54.
    Roweis S, Saul L (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(22):2323–2326Google Scholar
  55. 55.
    Samaria F, Harter A (1994) Parameterization of a Stochastic Model for Human Face Identification. 2nd IEEE Workshop on Applications of Computer Vision, SarasotaGoogle Scholar
  56. 56.
    Schölkopf B, Smola A, Müller KR (1999) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10:1299–1319Google Scholar
  57. 57.
    See KW, Loke KS, Lee PA, Loe KF (2007) Image reconstruction using various discrete orthogonal polynomials in comparison with DCT. Appl Math Comput 193(2):346–359MathSciNetzbMATHGoogle Scholar
  58. 58.
    Shan C (2012) Learning local binary patterns for gender classification on real-world face images. Pattern Recogn Lett 33(4):431–437Google Scholar
  59. 59.
    Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis Comput 27(6), pp. 803–816. Available: http://www.sciencedirect.com/science/article/pii/S0262885608001844
  60. 60.
    Shih HC (2013) Robust gender classification using a precise patch histogram. Pattern Recogn 46(2):519–528MathSciNetGoogle Scholar
  61. 61.
    Stajnko D, Rakun J, Blanke M (2009) Modelling apple fruit yield using image analysis for fruit color, shape and texture. Eur J Hortic Sci 74(6):260–267Google Scholar
  62. 62.
    Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans Image Process 19(6):1635–1650MathSciNetzbMATHGoogle Scholar
  63. 63.
    Tenenbaum J, Silva V, Langford J (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(22):2319–2323Google Scholar
  64. 64.
    Tseng S (2003) Comparison of holistic and feature based approaches to face recognition. MSc Thesis, Royal Melbourne Institute of Technology University, Melbourne, VictoriaGoogle Scholar
  65. 65.
    Turk MA, Pentland AP (1991) Eigenfaces for Recognition. J Cogn Neurosci 3(1):71–86Google Scholar
  66. 66.
    Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57:137–154Google Scholar
  67. 67.
    Wang L, Healey G (1998) Using Zernike Moments for the Illumination and Geometry Invariant Classification of Multispectral Texture. IEEE Trans Image Process 7(2):196–203Google Scholar
  68. 68.
    Wang W, Li C (2014) Size estimation of sweet onions using consumer-grade RGB-depth sensor. J Food Eng 142:153–162Google Scholar
  69. 69.
    Wang X, Tang X (2004) A unified framework for subspace face recognition. IEEE Trans Pattern Anal Mach Intell 26(9):1222–1228Google Scholar
  70. 70.
    Wolf L, Hassner T, Taigman Y (2011) Effective unconstrained face recognition bycombining multiple descriptors and learned background statistics. IEEE Trans Pattern Anal Mach Intell 33(10):1978–1990Google Scholar
  71. 71.
    Xia B, Amor BB, Drira H, Daoudi M, Ballihi L (2015) Combining face averageness and symmetry for 3D-based gender classification. Pattern Recogn 48(3):746–758Google Scholar
  72. 72.
    Yan S, Xu D, Zhang B, Zhang H, Yang Q, Lin S (2007) Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Trans Pattern Anal Mach Intell 29(1):40–51Google Scholar
  73. 73.
    Yang S, Bhanu B (2011) Facial expression recognition using emotion avatar image. IEEE Int Conf Autom Face Gesture Recognit Workshops (FG):866–871Google Scholar
  74. 74.
    Yin QB, Kim JN (2008) Rotation-invariant texture classification using circular Gabor wavelets based local and global features. Chin J Electron 17(4):646–648Google Scholar
  75. 75.
    Zaim A, Sawalha A, Quweider M, Iglesias J, Tang R (2006) A New Method for Iris Recognition Using Gray-level Co-occurrence Matrix. In: IEEE International Conf. on Electro/Information Technology, pp. 350–353Google Scholar
  76. 76.
    Zhang B, Gao Y, Zhao S, Liu J (2010) Local Derivative Pattern Versus Local Binary Pattern: Face Recognition with High-Order Local Pattern Descriptor. IEEE Trans Image Process 19(2):533–543MathSciNetzbMATHGoogle Scholar
  77. 77.
    Zhang Q, Zhang J (2009) RGB Color Analysis for Face Detection. In: Book: Advances in Computer Science and IT, pp. 109–125, InTechGoogle Scholar
  78. 78.
    Zhao G, Ahonen T, Matas J, Pietikäinen M (2012) Rotation invariant image and video description with local binary pattern features. IEEE Trans Image Processing 21(4):1465–1477MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Software EngineeringIstanbul Aydin UniversityIstanbulTurkey
  2. 2.Department of Computer EngineeringIstanbul Arel UniversityIstanbulTurkey

Personalised recommendations