Skip to main content
Log in

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper a novel multikernel deterministic extreme learning machine (ELM) and its variants are developed for classification of non-linear problems. Over a decade ELM is proved to be efficacious learning algorithms, but due to the non-deterministic and single kernel dependent feature mapping proprietary, it cannot be efficiently applied to real time classification problems that require invariant output solution. We address this problem by analytically calculation of input and hidden layer parameters for achieving the deterministic solution and exploiting the data fusion proficiency of multiple kernel learning. This investigation originates a novel deterministic ELM with single layer architecture in which kernel function is aggregation of linear combination of disparate base kernels. The weight of kernels depends upon perspicacity of problem and is empirically calculated. To further enhance the performance we utilize the capabilities of fuzzy set to find the pixel-wise coalition of face images with different classes. This handles the uncertainty involved in face recognition under varying environment condition. The pixel-wise membership value extracts the unseen information from images up to significant extent. The validity of the proposed approach is tested extensively on diverse set of face databases: databases with and without illumination variations and discrete types of kernels. The proposed algorithms achieve 100% recognition rate for Yale database, when seven and eight images per identity are considered for training. Also, the superior recognition rate is achieved for AT & T, Georgia Tech and AR databases, when compared with contemporary methods that prove the efficacy of proposed approaches in uncontrolled conditions significantly.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Ahlawat S, Choudhary A, Nayyar A, Singh S, Yoon B (2020) Improved handwritten digit recognition using convolutional neural networks (CNN). Sensors 20:3344

    Article  Google Scholar 

  2. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. In: Eur Conf Comput Vis. pp. 469–481

  3. Ahuja B, Vishwakarma VP (2018) Optimised multikernels based extreme learning machine for face recognition. Int J Appl Pattern Recognit 5:330–340

    Article  Google Scholar 

  4. Ahuja B, Vishwakarma VP (2019) Local feature extraction based KELM for face recognition. In: 2019 twelfth Int Conf Contemp Comput. pp. 1–5

  5. Ahuja B, Vishwakarma VP (2020) Local binary pattern based feature extraction with KELM for face identification. In: 2020 6th Int. Conf. Signal Process. Commun. pp. 91–95

  6. Aiolli F, Donini M (2015) EasyMKL: a scalable multiple kernel learning algorithm. Neurocomputing 169:215–224

    Article  Google Scholar 

  7. Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44:525–536

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  9. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge university press

  10. Brunelli R, Poggio T (1993) Face recognition: features versus templates. IEEE Trans Pattern Anal Mach Intell 15:1042–1052

    Article  Google Scholar 

  11. Bucak SS, Jin R, Jain AK (2013) Multiple kernel learning for visual object recognition: a review. IEEE Trans Pattern Anal Mach Intell 36:1354–1369

    Google Scholar 

  12. Chen L, Man H, Nefian AV (2005) Face recognition based on multi-class mapping of fisher scores. Pattern Recogn 38:799–811

    Article  Google Scholar 

  13. Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man, Cybern Part B 36:458–466

    Article  Google Scholar 

  14. De Siqueira FR, Schwartz WR, Pedrini H (2013) Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120:336–345

    Article  Google Scholar 

  15. Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. In: 2009 IEEE Symp Comput Intell data Min pp. 389–395

  16. Deng C, Han Y, Zhao B (2019) High-performance visual tracking with extreme learning machine framework. IEEE Trans Cybern.

  17. Déniz O, Bueno G, Salido J, la Torre F (2011) Face recognition using histograms of oriented gradients. Pattern Recogn Lett 32:1598–1603

    Article  Google Scholar 

  18. Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res 6:615–637

    MathSciNet  MATH  Google Scholar 

  19. Fan X, Xiang C, Chen C, et al (2020) BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning. IEEE Trans Mob Comput

  20. Feng G, Huang G-B, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20:1352–1357

    Article  Google Scholar 

  21. Gadekallu TR, Rajput DS, Reddy MPK, et al (2020) A novel PCA--whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Process 1–14.

  22. Gönen M, Alpaydin E (2011) Multiple kernel learning algorithms. J Mach Learn Res 12:2211–2268

    MathSciNet  MATH  Google Scholar 

  23. Gonzalez RC, Woods RE, others (2002) Digital image processing [M]. Publ house Electron Ind 141

  24. Guo P (2018) A vest of the pseudoinverse learning algorithm. arXiv Prepr. arXiv1805.07828

  25. Guo P, Lyu MR, Mastorakis NE (2001) Pseudoinverse learning algorithm for feedforward neural networks. Adv Neural Networks Appl.

  26. Han F, Huang D-S (2006) Improved extreme learning machine for function approximation by encoding a priori information. Neurocomputing 69:2369–2373

    Article  Google Scholar 

  27. Han H-G, Wang L-D, Qiao J-F (2014) Hierarchical extreme learning machine for feedforward neural network. Neurocomputing 128:128–135

    Article  Google Scholar 

  28. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12:55–67

    Article  MATH  Google Scholar 

  29. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Neural Networks, 2004. Proceedings. 2004 IEEE Int. Jt Conf pp 985–990

  30. Huang G-B, Zhu Q-Y, Mao KZ et al (2006) Can threshold networks be trained directly? IEEE Trans Circuits Syst II Express Briefs 53:187–191

    Article  Google Scholar 

  31. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  32. Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71:576–583

    Article  Google Scholar 

  33. Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man, Cybern Part B 42:513–529

    Article  Google Scholar 

  34. Huang Z, Yu Y, Gu J, Liu H (2017) An efficient method for traffic sign recognition based on extreme learning machine. IEEE Trans Cybern 47:920–933

    Article  Google Scholar 

  35. Jian Y, Huang D, Yan J, Lu K, Huang Y, Wen T, Zeng T, Zhong S, Xie Q (2017) A novel extreme learning machine classification model for e-nose application based on the multiple kernel approach. Sensors 17:1434

    Article  Google Scholar 

  36. Khare N, Devan P, Chowdhary CL, Bhattacharya S, Singh G, Singh S, Yoon B (2020) SMO-DNN: spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electronics 9:692

    Article  Google Scholar 

  37. Kim D-J, Bien Z (2008) Design of “personalized” classifier using soft computing techniques for “personalized” facial expression recognition. IEEE Trans Fuzzy Syst 16:874–885

    Article  Google Scholar 

  38. Klir G, Yuan B (1995) Fuzzy sets and fuzzy logic. Prentice hall New Jersey

  39. Li SZ, Anil K (2005) Jain. Handbook of Face Recognition.

  40. Li X, Mao W, Jiang W (2016) Multiple-kernel-learning-based extreme learning machine for classification design. Neural Comput Appl 27:175–184

    Article  Google Scholar 

  41. Li Y, Hu H, Zhu Z, Zhou G (2020) SCANet: sensor-based continuous authentication with two-stream convolutional neural networks. ACM Trans Sens Networks 16:1–27

    Article  Google Scholar 

  42. Li Y, Zou B, Deng S, Zhou G (2020) Using feature fusion strategies in continuous authentication on smartphones. IEEE Internet Comput 24:49–56

    Article  Google Scholar 

  43. Liu X, Wang L, Huang G-B, Zhang J, Yin J (2015) Multiple kernel extreme learning machine. Neurocomputing 149:253–264

    Article  Google Scholar 

  44. Lu C, Ke H, Zhang G, Mei Y, Xu H (2019) An improved weighted extreme learning machine for imbalanced data classification. Memetic Comput 11:27–34

    Article  Google Scholar 

  45. Martínez JM, Escandell-Montero P, Soria-Olivas E et al (2011) Regularized extreme learning machine for regression problems. Neurocomputing 74:3716–3721

    Article  Google Scholar 

  46. Miche Y, Sorjamaa A, Bas P et al (2009) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21:158–162

    Article  Google Scholar 

  47. Ojala T, Pietikäinen M, Mäenpää T (2000) Gray scale and rotation invariant texture classification with local binary patterns. Eur Conf Comput Vis, In, pp 404–420

  48. Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications.

  49. Rong H-J, Huang G-B, Sundararajan N, Saratchandran P (2009) Online sequential fuzzy extreme learning machine for function approximation and classification problems. IEEE Trans Syst Man, Cybern Part B 39:1067–1072

    Article  Google Scholar 

  50. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Appl. Comput. Vision, 1994., Proc. Second IEEE Work. pp 138–142

  51. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45:427–437

    Article  Google Scholar 

  52. Tang J, Deng C, Huang G-B (2016) Extreme learning machine for multilayer perceptron. IEEE Trans neural networks Learn Syst 27:809–821

    Article  MathSciNet  Google Scholar 

  53. Vishwakarma VP (2015) Illumination normalization using fuzzy filter in DCT domain for face recognition. Int J Mach Learn Cybern 6:17–34

    Article  Google Scholar 

  54. Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing 74:2483–2490

    Article  Google Scholar 

  55. Wong CM, Vong CM, Wong PK, Cao J (2016) Kernel-based multilayer extreme learning machines for representation learning. IEEE Trans neural networks Learn Syst 29:757–762

    Article  MathSciNet  Google Scholar 

  56. Xie X, Zheng W-S, Lai J et al (2010) Normalization of face illumination based on large-and small-scale features. IEEE Trans Image Process 20:1807–1821

    Article  MathSciNet  MATH  Google Scholar 

  57. Xu Z, Jin R, Yang H, et al (2010) Simple and efficient multiple kernel learning by group lasso. In: Proc. 27th Int. Conf. Mach. Learn. pp 1175–1182

  58. Yang H, Xu Z, Ye J et al (2011) Efficient sparse generalized multiple kernel learning. IEEE Trans Neural Netw 22:433–446

    Article  Google Scholar 

  59. Zadeh LA (1988) Fuzzy logic. Computer (Long Beach Calif) 21:83–93

    Google Scholar 

  60. Zadeh LA (1999) Fuzzy logic= computing with words. In: Comput. with words information/intelligent Syst. 1. Springer, pp 3–23

  61. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35:399–458

    Article  Google Scholar 

  62. Zhou C, Wang L, Zhang Q, Wei X (2013) Face recognition based on PCA image reconstruction and LDA. Optik (Stuttg) 124:5599–5603

    Article  Google Scholar 

  63. Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recogn 38:1759–1763

    Article  MATH  Google Scholar 

  64. Zhuang J, Tsang IW, Hoi SCH (2011) Two-layer multiple kernel learning. Proc Fourteenth Int Conf Artif Intell Stat, In, pp 909–917

  65. Zong W, Huang G-B (2011) Face recognition based on extreme learning machine. Neurocomputing 74:2541–2551

    Article  Google Scholar 

  66. Zong W, Zhou H, Huang G-B, Lin Z (2011) Face recognition based on kernelized extreme learning machine. In: Int Conf Auton Intell Syst. pp. 263–272

  67. Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242

    Article  Google Scholar 

  68. Zou C, Kou KI, Wang Y (2016) Quaternion collaborative and sparse representation with application to color face recognition. IEEE Trans Image Process 25:3287–3302

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Virendra P. Vishwakarma.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ahuja, B., Vishwakarma, V.P. Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification. Multimed Tools Appl 80, 32423–32447 (2021). https://doi.org/10.1007/s11042-021-11097-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11097-3

Keywords

Navigation