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Color-based feature extraction with application to facial recognition using tensor-matrix and tensor-tensor analysis

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Abstract

In this article, two main goals are investigated. 1) Developing the capabilities of tensor analysis into machine learning and pattern recognition applications such as facial recognition. 2) Answering a controversial question about the usefulness of the color information for face recognition despite small color sub-space which facial pictures possess. Firstly, an algorithm based on merely color information, extracted by tensor-matrix decomposition, is proposed. The proposed method reveals the potential of this kind of information. Secondly, another algorithm is proposed which utilizes both color and structural information by which the recognition rate is improved. Finally, by presenting the third algorithm based on tensor-tensor analysis and introducing the Eigen-Tensor concept, the task is fulfilled. The third algorithm is able to extract the color information and accomplish the dimensionality reduction simultaneously. To validate their robustness, these three proposed algorithms are evaluated by three different databases with different properties. The results represent the capabilities of tensor-tensor analysis, in comparison with other approaches. It is also proven that in spite of using the ordinary classifiers, the recognition rate more than 94% is achievable.

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References

  1. Adachi K, Adachi K (2018) Erratum to: Matrix-based introduction to multivariate data analysis

  2. AlZu’bi S, Amira A (2010) 3D medical volume segmentation using hybrid multiresolution statistical approaches. Advances in Artificial Intelligence, 2010

  3. AlZu’bi S, Hawashin B, Mujahed M, Jararweh Y, Gupta BB (2019) An efficient employment of internet of multimedia things in smart and future agriculture. Multimed Tools Appl

  4. AR face database webpage. [Online]. Available: http://www2.ece.ohio-state.edu/~aleix/ARdatabase. Accessed: 30-Jun-2018

  5. Bader BW, Kolda TG (2007) Tensor decompositions , the MATLAB tensor toolbox , and applications to data analysis tensor decompositions

  6. Benitez-Quiroz CF, Srinivasan R, Martinez AM (2018) Facial color is an efficient mechanism to visually transmit emotion. Proc Natl Acad Sci 201716084

  7. Bowen RM, Bowen CRM (2014) Part 1: linear algebra

  8. Briefs S, Computer IN (2016) Matrix and tensor factorization techniques for recommender systems. Springer International Publishing

  9. Choi JY, Ro YM, Plataniotis KN (2009) Color face recognition for degraded face images. IEEE Trans Syst Man Cybern B 39(5):1217–1230

    Article  Google Scholar 

  10. Choi JY, Ro YM, Plataniotis KN (2011) A comparative study of preprocessing mismatch effects in color image based face recognition. Pattern Recogn 44(2):412–430

    Article  Google Scholar 

  11. Choi JY, Ro YM, Plataniotis KN (2012) Color local texture features for color face recognition. IEEE Trans Image Process 21(3):1366–1380

    Article  MathSciNet  Google Scholar 

  12. Cichocki A et al (2015) Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Signal Process Mag 32(2):145–163

    Article  Google Scholar 

  13. Eldén L (2007) Matrix methods in data mining and pattern recognition (Fundamentals of algorithms)

  14. Fallis A (2013) Linear algebra and its applications 4Ed. J Chem Inf Model 53(9):1689–1699

    Google Scholar 

  15. Geometry R, Analysis G (2013) Analysis of multivariate and high-dimensional data

  16. Georgia Tech color face database. [Online]. Available: http://www.anefian.com/research/face_reco.htm. Accessed: 30-Jun-2018

  17. Hu K, Liu Y, Dong Q, Liu H, Xing G (2014) Color face image decomposition under complex lighting conditions. Vis Comput 30(6–8):685–695

    Article  Google Scholar 

  18. Iozzi A (2014) Multilinear algebra and applications

  19. Jing X, Liu Q, Lan C, Man J, Li S, Zhang D (2010) Holistic orthogonal analysis of discriminant transforms for color face recognition. In: 2010 IEEE international conference on image processing, pp 3841–3844

    Chapter  Google Scholar 

  20. Kemp R, Pike G, White P, Musselman A (1996) Perception and recognition of normal and negative faces: the role of shape from shading and pigmentation cues. Perception 25(1):37–52, Jan

    Article  Google Scholar 

  21. Kiers HAL (2000) Towards a standarized notation and terminolgy in multiway analysis 14(3)

  22. Kilmer ME, Martin CD (2011) Factorization strategies for third-order tensors. 435(3)

  23. Kilmer ME, Braman K, Hao N, Hoover RC (2013) Third-order tensors as operators on matrices: a theoretical and computational framework with applications in imaging. 34(1)

  24. Kim YD, Choi S (2007, August) Color face tensor factorization and slicing for illumination-robust recognition. In International Conference on Biometrics (pp.19–28). Springer, Berlin, Heidelberg

  25. Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  MathSciNet  Google Scholar 

  26. Kroonenberg PM (2007) Applied multiway data analysis

  27. Lajevardi SM, Wu HR (2012) Facial expression recognition in perceptual color space. IEEE Trans Image Process 21(8):3721–3733

    Article  MathSciNet  Google Scholar 

  28. Liu C (2008) Learning the uncorrelated, independent, and discriminating color spaces for face recognition. IEEE Transactions on Information Forensics and Security 3(2):213–222

  29. Liu Z, Liu C (2010) Fusion of color, local spatial and global frequency information for face recognition. Pattern Recognit 43(8):2882–2890

    Article  Google Scholar 

  30. Liu Z, Qiu Y, Peng Y, Pu J, Zhang X (2017) Quaternion based maximum margin criterion method for color face recognition. Neural Process Lett 45(3):913–923

    Article  Google Scholar 

  31. Lu H, Plataniotis KN, Venetsanopoulos AN (2011) A survey of multilinear subspace learning for tensor data. Pattern Recogn 44(7):1540–1551

    Article  Google Scholar 

  32. Lu H, Plataniotis KN, Venetsanopoulos A (2013) Multilinear subspace learning: dimensionality reduction of multidimensional data. Chapman and Hall/CRC

  33. Mahmoud S, Al F (2011) 3D multiresolution statistical approaches for accelerated medical image and volume segmentation

  34. Man J, Jing X, Liu Q, Yao Y, Li K, Yang J (2011) Color face recognition based on statistically orthogonal analysis of projection transforms. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics), vol. 7098 LNCS, p 58–65

  35. Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M (1997) The DET curve in assessment of detection task performance. National Inst of Standards and Technology Gaithersburg MD

  36. Martin CD, Shafer R, LaRue B (2013) An order-$p$ tensor factorization with applications in imaging. SIAM J Sci Comput 35(1):A474–A490

    Article  MathSciNet  Google Scholar 

  37. Martinez AM, Benavente R. AR face database, 2000

  38. Matrix N (2008) Non-negative matrix factorization: theory and methods multimedia information and signal processing

    Google Scholar 

  39. Mørup M (2011) Applications of tensor (multiway array) factorizations and decompositions in data mining. Wiley Interdiscip Reviews: Data Mining and Knowledge Discovery 1(1):24–40

  40. Paredes BR (2014) Multitask and transfer learning for multi-aspect data. University College, London

    Google Scholar 

  41. Qi L, Chen H, Chen Y (2018) Tensor eigenvalues and their applications, vol 39

    Book  Google Scholar 

  42. Rabanser S, Shchur O, Günnemann S (2017) Introduction to tensor decompositions and their applications in machine learning

  43. Rahmanian Koushkaki H, Salehi MR, Abiri E (2017) Automatic colourization of grayscale images based on tensor decomposition. Multimed Tools Appl

  44. Rezghi M, Eldén L (2011) Diagonalization of tensors with circulant structure. Linear Algebra Appl 435(3):422–447

    Article  MathSciNet  Google Scholar 

  45. Sakata T (2016) Springer briefs in statistics applied matrix and tensor variate data analysis. SpringerBriefs in Statistics

  46. A. Shashua, (2007) Tensor methods for machine learning, computer vision , and computer graphics factorizations of multi-dimensional arrays

    Google Scholar 

  47. Shen X, Yuan Y-H, Shen F, Xu Y, Sun Q-S (2018) A novel multi-view dimensionality reduction and recognition framework with applications to face recognition. J Vis Commun Image Represent 53:161–170

    Article  Google Scholar 

  48. Sidiropoulos ND, De Lathauwer L, Fu X, Huang K, Papalexakis EE, Faloutsos C (2017) Tensor decomposition for signal processing and. Mach Learn 65(13)

  49. Smilde A, Bro R, Geladi P (2004) Multi-way analysis with applications in the chemical sciences

  50. Sun Y, Jia H, Hu Y, Yin B (2014) Color face recognition based on color image correlation similarity discriminant model. Multimed Tools Appl 73(3):2063–2079

    Article  Google Scholar 

  51. Thomaz CE (2018) FEI face database. Department of Electrical Engineering Centro Universitario da FEI, São Bernardo do Campo, São Paulo, Brazil. [Online]. Available: http://fei.edu.br/~cet/facedatabase.html. Accessed: 30-Jun-2018

  52. Torres L, Reutter JY, Lorente L (1999) The importance of the color information in face recognition. In: Proceedings international conference on image processing (cat. 99CH36348), vol 3, pp 627–631

    Chapter  Google Scholar 

  53. Turk MA, Pentland AP (1991, June) Face recognition using eigenfaces. In Proceedings. 1991 IEEE Computer Vision and Pattern Recognition (pp. 586–591)

  54. Vasilescu MAO (2012) A multilinear ( tensor ) algebraic framework for computer graphics. Computer Vision, and Machine Learning (Doctoral dissertation, University of Toronto)

  55. Wang CWC, Yin BYB, Bai XBX, Sun YSY (2008) Color face recognition based on 2DPCA. In: 2008 19th International Conference on Pattern Recognition, pp. 8–11

  56. Wang S-J, Yang J, Zhang N, Zhou C-G (2011) Tensor discriminant color space for face recognition. IEEE Trans Image Process 20(9):2490–2501

    Article  MathSciNet  Google Scholar 

  57. Wang SJ, Yang J, Sun MF, Peng XJ, Sun MM, Zhou CG (2012) Sparse tensor discriminant color space for face verification. IEEE Transactions on Neural Networks and Learning System 23(6):876–888

  58. Wu F et al (2016) Uncorrelated multi-set feature learning for color face recognition. Pattern Recogn 60:630–646

    Article  Google Scholar 

  59. Xiang X, Yang J, Chen Q (2015) Color face recognition by PCA-like approach. Neurocomputing 152:231–235

    Article  Google Scholar 

  60. Xiao X, Zhou Y (2018, July) Quaternion sparse discriminant analysis for color face recognition. In 2018 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1–6) IEEE

  61. Yang J, Liu C (2008) Color image discriminant models and algorithms for face recognition. IEEE Trans Neural Netw 19(12):2088–2098

    Article  Google Scholar 

  62. Yang J, Zhang D, Frangi AF, Yang JY (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

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Author notes

  1. Hassan Rahmanian Koushkaki & Ebrahim Abiri

    Present address: Electrical and Electronics Engineering Department, Shiraz University of Technology (SUTECH), Shiraz, Iran

Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Shiraz University of Technology (SUTECH), Shiraz, Iran

    Mohammad Reza Salehi

  2. Second Company Department, Regional Information Center for Science & Technology(RICeST), Jam-e-Jam Ave, Shiraz, Iran

    Mohammad Reza Salehi

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  1. Hassan Rahmanian Koushkaki
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Correspondence to Mohammad Reza Salehi.

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Koushkaki, H.R., Salehi, M.R. & Abiri, E. Color-based feature extraction with application to facial recognition using tensor-matrix and tensor-tensor analysis. Multimed Tools Appl 79, 5829–5858 (2020). https://doi.org/10.1007/s11042-019-08177-w

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