On the complex domain deep machine learning for face recognition

Abstract

Biometric based verification and recognition has become the center of attention for many significant applications for security conscious societies, as it is believed that biometrics can provide accurate and reliable identification. The face biometrics are one that possesses the merits of both high accuracy and low intrusiveness. An efficient machine recognition of human faces in big dataset is both important and challenging tasks. This paper addresses an intelligent face recognition system that is pose invariant and can recognize multi-expression, occluded and blurred faces through efficient but compact deep learning. Superior functionality of neural network in a complex domain has been observed in recent researches. My work presents a new approach, which is the fusion of higher-order novel neuron models with multivariate statistical techniques in a complex domain with a sole goal of improving performance of biometric systems. This also aims at reducing the computational cost and providing a faster recognition system. This paper presents the formal algorithms for feature extraction with multivariate statistical techniques in complex domain and compare them their real domain counterpart. This paper also presents a classifier structure (OCON : One-Class-in-One-Neuron) which contains an ensemble of novel higher order neurons, which drastically reduces the complexity of proposed learning machine because only single neuron is sufficient to recognize a subject in the database. This novel fusion in the proposed deep learning machine has thoroughly presented its superiority over a wide spectrum of experiments. Advanced deep learning capabilities, and complex domain implementation in particular, are significantly advancing state-of-art in computer vision and pattern recognition.

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References

  1. 1.

    Abate AF, Nappi M, Riccio D, Sabatino G (2007) 2D and 3D face recognition: a survey. Pattern Recognition. Letters 28:1885–1906

    Google Scholar 

  2. 2.

    Ahonen T, Pietikainen M, Hadid A (2004) Face recognition based on the appearance of local regions. In: 17th international conference on pattern recognition, vol 3, pp 153–156

  3. 3.

    Aitkenheada MJ, Mcdonald AJS (2003) A neural network face recognition system. Eng Appl Artif Intell 16(3):167–176

    Article  Google Scholar 

  4. 4.

    Aizenberg I, Moraga C (2007) Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Comput 11(2):169–183

    Article  Google Scholar 

  5. 5.

    Anemuller J, Sejnowski T, Makeig S (2003) Complex independent component analysis of frequency-domain electroencephalographic data. Neural Netw 16(9):1311–1323

    Article  Google Scholar 

  6. 6.

    Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  7. 7.

    Bell AJ, Sejnowski TJ (1995) An information-maximization approach to blind separation and blind deconvolution. Neural Comput 7(6):1129–1159

    Article  Google Scholar 

  8. 8.

    Bhattacharjee D, Basu DK, Nasipuri M, Kundu M (2010) Soft Comput 14(Human face recognition using multilayer perceptron):559–570. Springer

    Article  Google Scholar 

  9. 9.

    Brown JW, Churchill RV (2003) Complex variables and applications, VII th ed. Mc Graw Hill

  10. 10.

    Calhoun V, Adali T (2006) Complex infomax: convergence and approximation of infomax with complex nonlinearities. VLSI Signal Process, Springer Science 44:173–190

    MATH  Google Scholar 

  11. 11.

    Calhoun V, Adali T, Pearlson GD, Pekar JJ (2002) ON complex infomax applied to complex FMRI data. In: Proceedings of ICASSP, Orlando

  12. 12.

    Calhoun VD, Adali T (2006) Unmixing fMRI with independent component analysis. IEEE Eng Med Biol Mag 25(2):79–90

    Article  Google Scholar 

  13. 13.

    Cevikalp H, Neamtu M, Wilkes M, Barkana A (2005) Discriminative common vectors for face recognition. IEEE Trans PAMI 27(1):1–9

    Article  Google Scholar 

  14. 14.

    Er MJ, Chen W, Wu S (2005) High-speed face recognition based on discrete cosine transform and RBF neural networks. IEEE Trans Neural Netw 16(3):679–691

    Article  Google Scholar 

  15. 15.

    Faijul Amin M, Murase K (2008) Single-layered complex-valued neural network for real-valued classification problems. Neurocomputing

  16. 16.

    Haddadnia J, Faez K, Moallem P (2001) Neural network based face recognition with moments invariant. In: IEEE international conference image processing, vol I, Thessaloniki, pp 710

  17. 17.

    Hahn SL (1996) Hilbert transforms in signal processing. Artech House, Boston

    Google Scholar 

  18. 18.

    Hietmeyer R (2000) Biometric identification promises fast and secure processing of airline passengers. The International Civil Aviation Organization Journal 55(9):10–11

    Google Scholar 

  19. 19.

    Hirose A (2006) Complex-valued neural networks. Springer-Verlag New York Inc, New York

    Google Scholar 

  20. 20.

    Huang D, Mi J (2007) A new constrained independent component analysis method. IEEE Trans Neural Netw 18(5):1532–1535

    Article  Google Scholar 

  21. 21.

    Jain V, Mukherjee A (2002) The Indian face database, http://vis-www.cs.umass.edu/vidit/Indianfacedatabase

  22. 22.

    Kasar MM, Bhattacharyya D, Kim TH (2016) Face recognition using neural network: a review. Int J Secur Appl 10(3):81–100

    Google Scholar 

  23. 23.

    Kwak K, Pedrycz W (2007) Face recognition using an enhanced independent component analysis approach. IEEE Trans Neural Netw 18(2):530–541

    Article  Google Scholar 

  24. 24.

    Lee CC, Chung PC, Tsai JR, Chang CI (1999) Robust radial basis function neural network. IEEE Trans Syst, Man Cybern B Cybern 29(6)

  25. 25.

    Lua Wei, Rajapakse JC (2006) ICA with reference. neurocomputing 69:2244–2257

    Article  Google Scholar 

  26. 26.

    Mandic D, Goh VSL (2009) Complex valued nonlinear adaptive filters: noncircularity, widely linear and neural models. Wiley

  27. 27.

    Marcialis GL, Roli F (2004) Fusion of appearance based face recognition algorithms. Pattern Anal Appl 7 (2):151–163

    MathSciNet  Article  Google Scholar 

  28. 28.

    Mel BW (1995) Information processing in dendritic trees. Neural Comput 6:1031–1085

    Article  MATH  Google Scholar 

  29. 29.

    Member JE, Koivunen V (2006) Complex random vectors and ICA models: identifiability, Uniqueness and Separability. IEEE Trans Inf Theory 52(3):596–609

    MathSciNet  Google Scholar 

  30. 30.

    Moon HM, Seo CH, Pan SB (2016) A face recognition system based on convolution neural network using multiple distance face, Soft Comput. Springer. doi:10.1007/s00500-016-2095-0

  31. 31.

    Nitta T (1997) An extension of the back-propagation algorithm to complex numbers. Neural Netw 10 (8):1391–1415

    Article  Google Scholar 

  32. 32.

    Novey M, Adali T (2008) Complex ICA by negentropy maximization. IEEE Trans Neural Netw 19 (4):596–609

    Article  Google Scholar 

  33. 33.

    Oja E, Yuan Z (2006) The fastICA algorithm revisited: Convergence analysis. IEEE Trans Neural Netw 17(6):1370–1381

    Article  Google Scholar 

  34. 34.

    ORL face database, http://www.uk.research.att.com/facedatabase.html

  35. 35.

    Patil H, Kothari A, Bhurchandi K (2016) Expression invariant face recognition using semidecimated DWT, patch-LDSMT, feature and score level fusion. Appl Intell, Springer 44(4):913–930

    Article  Google Scholar 

  36. 36.

    Rattan SSP, Hsieh WW (2005) Complex-valued neural networks for nonlinear complex principal component analysis. Neural Netw 18:61–96

    Article  MATH  Google Scholar 

  37. 37.

    Saff EB, Snider (2003) Fundamentals of complex analysis with applications to engineering and science. Englewood Cliffs

  38. 38.

    Sing JK, Basu DK, Nasipuri M, Kundu M (2007) Face recognition using point symmetry distance-based RBF network. Appl Soft Comput 7:58–70

    Article  Google Scholar 

  39. 39.

    Srivastava Vivek, Tripathi BK, Pathak VK (2015) Hybrid computation model for intelligent system design by synergism of modified EFC with neural network. Int J Inf Technol Decis Mak, World Scientific 14(1):17–41

    Article  Google Scholar 

  40. 40.

    Srivastava Vivek, Tripathi BK, Pathak VK (2015) Neurological disorder identification by eye movement biometric using machine learning schemes. International Conference on Advances in Computing, Control and Networking, Bangkok

    Google Scholar 

  41. 41.

    Tripathi BK, Chandra B, Kalra PK The generalized product neuron model in complex domain. In: Advances in Neuro-information processing, vol 5507/2009, pp 867–876. Springer, Berlin

  42. 42.

    Tripathi BK, Chandra B, Kalra PK (2011) Complex generalized-mean neuron model and its Applications. Appl Soft Comput (Elsevier) 11(01):768–777

    Article  Google Scholar 

  43. 43.

    Tripathi BK, Kalra PK (2011) On efficient learning machine with root power mean neuron in complex domain. IEEE Trans Neural Netw 22(05):727–738

    Article  Google Scholar 

  44. 44.

    Tripathi BK, Kalra PK (2011) On the learning machine for three dimensional mapping. Neural Comput & Applic., Springer 20(01):105–111

    Article  Google Scholar 

  45. 45.

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

    Article  Google Scholar 

  46. 46.

    Yale University face database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html

  47. 47.

    Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: a literature survey. ACM Comput Surv 35(4):399–458

    Article  Google Scholar 

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Correspondence to B. K. Tripathi.

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Tripathi, B.K. On the complex domain deep machine learning for face recognition. Appl Intell 47, 382–396 (2017). https://doi.org/10.1007/s10489-017-0902-7

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Keywords

  • Complex independent component analysis (C I C A)
  • Complex principal component analysis (C P C A)
  • One-class-in-one-neuron (OCON)
  • Biometrics