Advertisement

Machine Recognition in Complex Domain

  • Bipin Kumar TripathiEmail author
Chapter
First Online:
  • 968 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 571)

Abstract

Machine recognition has drawn considerable interest and attention from researches in intelligent system design and computer vision communities over the recent past. Understandably there are a large number of commercial, law enforcement, control and forensic applications to this. We human beings have natural ability to recognize persons at a glance. Motivated by our remarkable ability, a series of attempts [1, 2, 3, 4] have been made to simulate this ability in machines. The development of human recognition system in machines is quite difficult because the natural objects are complex, multidimensional, and corresponds to environmental changes [3, 5, 6, 7]. There are two important issues that need to be addressed in machine recognition: (1) how the features are adopted to represent an object under environmental changes and (2) how we classify an object image based on a chosen representation. Over the years, researches have developed a number of methods for feature extraction and classification. All of these, however, have their own merits and demerits. Most of the work is related to the real domain. The outperformance of complex-valued neuron over conventional neuron has been well established in previous chapters. Few researchers have recently tried multivariate statistical techniques in the complex domain, like complex principal component analysis (PCA) for 2D vector field analysis [8] and complex independent component analysis (ICA) for performing source separation on functional magnetic resonance imaging data [9, 10]. But, no attempts have been made to develop techniques for feature extraction using their concepts. This chapter presents formal procedures for feature extraction using unsupervised learning techniques in complex domain. Efficient learning and better precision in result offered by feature extractor and classifier, considering simulations in complex domain, figure out their technical benefits over conventional methods. Notably, the success of machine recognition is limited by variations in features resulting from the natural environment. These may be due to instrument distortion, acquisition in an outdoor environment, different noises, complex background, occlusion and illumination. A solid set of examples presented in this chapter demonstrate the superiority of feature representation and classification in complex domain.

Keywords

Feature Extraction Independent Component Analysis Independent Component Analysis Complex Domain False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Daugman, J.: Face dection : a survey. Comput. Vis. Image Underst. 83(3), 236–274Google Scholar
  2. 2.
    Chellappa, R., Wilson, C.L., Sirohey, S.: Human and machine recognition of faces: a survey. Proc. IEEE 83, 705–740 (1995)CrossRefGoogle Scholar
  3. 3.
    Kong, S.G., Heo, J., Abidi, B.R., Paik, J., Abidi, M.A.: Recent advances in visual and infrared face recognition-a review. Comput. Vis. Image Underst. 97, 103–135 (2005)CrossRefGoogle Scholar
  4. 4.
    Bhattacharjee, D., Basu, D.K., Nasipuri, N., Kundu, M.: Human face recognition using multilayer perceptron. Soft Comput. 14, 559–570 (2010)Google Scholar
  5. 5.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: a survey. Pattern Recogn. Lett. 28, 1885–1906 (2007)CrossRefGoogle Scholar
  6. 6.
    Chen, L.F., Liao, H.M., Lin, J., Han, C.: Why recognition in a statistic-based face recognition system should be based on the pure face portion: a probabilistic decision-based proof. Pattern Recogn. 34(7), 1393–1403 (2001)CrossRefzbMATHGoogle Scholar
  7. 7.
    Sebe, N., Lew, M.S., Huijsmans, D.P.: Toward improved ranking metrics. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1132–1143 (2000)Google Scholar
  8. 8.
    Rattan, S.S.P., Hsieh, W.W.: Complex-valued neural networks for nonlinear complex principal component analysis. Neural Networks 18, 61–96 (2005)CrossRefzbMATHGoogle Scholar
  9. 9.
    Anemuller, J., Sejnowski, T., Makeig, S.: Complex independent component analysis of frequency-domain electroencephalographic data. Neural Networks 16(9), 1311–1323 (2003)Google Scholar
  10. 10.
    Calhoun, V.D., Adal, T.: Unmixing fMRI with independent component analysis. IEEE Eng. Med. Biol. Mag. 25(2), 79–90 (2006)Google Scholar
  11. 11.
    Hietmeyer, R.: Biometric identification promises fast and secure processing of airline passengers. Int. Civil Aviat. Org. J. 55(9), 10–11 (2000)Google Scholar
  12. 12.
    Hahn, S.L.: Hilbert Transforms in Signal Processing. Artech House, Boston, MA (1996)zbMATHGoogle Scholar
  13. 13.
    Lanitis, A., Taylor, C.J., Cootes, T.F.: Towards automatic simulation of ageing effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)CrossRefGoogle Scholar
  14. 14.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature Survey. ACM Comput. Surv. 35(4), 399–458 (2003)Google Scholar
  15. 15.
    Wiskott, L., Fellous, J.M., Kruger, K., Von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)CrossRefGoogle Scholar
  16. 16.
    Vetter, T., Poggio, T.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 733–742 (1997)CrossRefGoogle Scholar
  17. 17.
    Ara, V.N., Monson, H.H.: Face recognition using an embedded HMM. In: Proceedings of International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 19–24 (1999)Google Scholar
  18. 18.
    Bevilacqua, V., Cariello, L., Carro, G., Daleno, D., Mastronardi, G.: A face recognition system based on Pseudo 2D HMM applied to neural network coefficients. Soft Comput. 12, 615–621 (2008)CrossRefGoogle Scholar
  19. 19.
    Volker, B., Sami, R., Thomas, V.: Face identification across different poses and illuminations with a 3D morphable model. In: Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 202–207 (2002)Google Scholar
  20. 20.
    Cover, T., Thomas, J.: Elements of Information Theory. John Wiley and Sons, Chichester (1991)Google Scholar
  21. 21.
    Marcialis, G.L., Roli, F.: Fusion of appearance based face recognition algorithms. Pattern Anal. Appl. 7(2), 151–163 (2004)Google Scholar
  22. 22.
    Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. PAMI 27(1), 1–9 (2005)CrossRefGoogle Scholar
  23. 23.
    Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)Google Scholar
  24. 24.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)Google Scholar
  25. 25.
    Savvides, M, Vijaya Kumar, B.V.K., Khosla, P.K.: Eigenphases vs. eigenfaces. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 3, pp. 810–813 (2004)Google Scholar
  26. 26.
    Ahonen, T,. Pietikainen, M., Hadid, A.: Face recognition based on the appearance of local regions. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 3, pp. 153–156 (2004)Google Scholar
  27. 27.
    Liu, C.: Gabor-based kernel PCA with fractional power polynomial models for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 572–581 (2004)CrossRefGoogle Scholar
  28. 28.
    Martinez, A.M., Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Anal. Mach. Intell. 23(2), 228–233 (2001)Google Scholar
  29. 29.
    Kim, T., Kittler, J.: Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 318–327 (2005)CrossRefGoogle Scholar
  30. 30.
    Dai, G., Qian, Y., Jia, S.: A Kernel fractional-step nonlinear discriminant analysis for pattern recognition. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 2, pp. 431–434 (2004)Google Scholar
  31. 31.
    Bartlett, M.S., Lades, H.M., Sejnowski, T.J.: Independent component representations for face recognition. Proc. of SPIE 3299, 528–539 (1998)CrossRefGoogle Scholar
  32. 32.
    Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley and Sons, New York (2002)Google Scholar
  33. 33.
    Kwak, K., Pedrycz, W.: Face recognition using an enhanced independent component analysis approach. IEEE Trans. Neural Network 18(2), 530–541 (2007)Google Scholar
  34. 34.
    Kim, J., Choi, J., Yi, J., Turk, M.: Effective representation using ICA for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1977–1981 (2005)CrossRefGoogle Scholar
  35. 35.
    Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons, New York (2001)Google Scholar
  36. 36.
    Huang, D., Mi, J.: A new constrained independent component analysis method. IEEE Trans. Neural Netw. 18(5), 1532–1535 (2007)Google Scholar
  37. 37.
    Wei, L., Jagath, C.R.: ICA with reference. Neurocomputing 69, 2244–2257 (2006)Google Scholar
  38. 38.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  39. 39.
    Er, M.J., Chen, W., Wu, S.: High-speed face recognition based on discrete cosine transform and RBF neural networks. IEEE Trans. Neural Netw. 16(3), 679–691 (2005)Google Scholar
  40. 40.
    Hyvarinen, A.: Survey on independent component analysis. Neural Comput. Surv. 2, 94–128 (1999)Google Scholar
  41. 41.
    Bell, A.J., Sejnowski, T.J.: An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1129–1159 (1995)CrossRefGoogle Scholar
  42. 42.
    Horel, J.D.: Complex principal component analysis: theory and examples. J. Clim. Appl. Meteorol. 23, 1660–1673 (1984)Google Scholar
  43. 43.
    Jan, E.M., Visa, K.: Complex random vectors and ICA models: identifiability, uniqueness and separability. IEEE Trans. Inf. Theory 52(3), 596–609 (2006)Google Scholar
  44. 44.
    Adal, T., Kim, T., Calhoun, V.: Independent component analysis by complex nonlinearities. In: Proceedings of International Conference on Acoustics Speech Signal Process, Montreal, ON, Canada, May 2004, vol. 5, pp. 525–528 (2004)Google Scholar
  45. 45.
    Huang, N.E.: Introduction to Hilbert-Huang transform and its associated mathematical problems. In: Huang, N.E., Attoh-Okine, N. (eds.) Hilbert-Huang Transform in Engineering, pp. 1–32. CRC Press, New York ( 2005)Google Scholar
  46. 46.
    Amores, J., Sebe, N., Radeva, D.P.: Boosting the distance estimation: application to the K-nearest neighbor classifier. Pattern Recogn. Lett. 27, 201 (2006)Google Scholar
  47. 47.
    Lawrence, S., Lee, G.C., Ah, C.T., Andrew, D.B.: Face recognition: a convolutional neural network approach. IEEE Trans. Neural Netw. 8, 98–113 (1997). (special issue on neural networks and pattern recognition)Google Scholar
  48. 48.
    Oh, B.J.: Face recognition by using neural network classifiers based on PCA and LDA. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, pp. 1699–1703, 10–12 Oct 2005Google Scholar
  49. 49.
    Sing, J.K., Basu, D.K., Nasipuri, M., Kundu, M.: Face recognition using point symmetry distance-based RBF network. Appl. Soft Comput. 7, 58–70 (2007)CrossRefGoogle Scholar
  50. 50.
    Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)Google Scholar
  51. 51.
    Aitkenheada, M.J., Mcdonald, A.J.S.: A neural network face recognition system. Eng. Appl. Artif. Intell. 16(3), 167–176 (2003)Google Scholar
  52. 52.
    Giacinto, G., Roli, F., Fumera, G.: Unsupervised learning of neural network ensembles for image classification. IEEE IJCNN 3, 155–159 (2000)Google Scholar
  53. 53.
    Haddadnia, J., Faez, K., Moallem, P.: Neural network based face recognition with moments invariant. In: Proceedings of IEEE International Conference on Image Processing, vol. I, pp. 1018–1021, Thessaloniki, Greece, 7–10 Oct 2001Google Scholar
  54. 54.
    Lee, T.W.: Independent Component Analysis: Theory and Applications. Kluwer, Boston, MA (1998)zbMATHGoogle Scholar
  55. 55.
    Oja, E., Yuan, Z.: The FastICA algorithm revisited: convergence analysis. IEEE Trans. Neural Networks 17(6), 1370–1381 (2006)Google Scholar
  56. 56.
    Nitta, T.: An extension of the back-propagation algorithm to complex numbers. Neural Netw. 10(8), 1391–1415 (1997)CrossRefGoogle Scholar
  57. 57.
    Calhoun, V., Adali, T.: Complex infomax: convergence and approximation of infomax with complex nonlinearities. VLSI Signal Process. Springer Sci. 44, 173–190 (2006)CrossRefzbMATHGoogle Scholar
  58. 58.
    Calhoun, V., Adali, T., Pearlson, G.D., Pekar, J.J.: ON complex infomax applied to complex FMRI data. In: Proceedings of ICASSP, Orlando, FL (2002)Google Scholar
  59. 59.
    Brown, J.W., Churchill, R.V.: Complex Variables and Applications, 7th edn. Mc Graw Hill, New York (2003)Google Scholar
  60. 60.
    Saff, E.B., Snider.: Fundamentals of Complex Analysis with Applications to Engineering and Science. Englewood Cliffs, New Jersey (2003)Google Scholar
  61. 61.
    Novey, M., Tlay, A.: Complex ICA by negentropy maximization. IEEE Trans. Neural Network 19(4), 596–609 (2008)Google Scholar
  62. 62.
  63. 63.
    Vidit, J., Amitabha, M.: The Indian face database. http://vis-www.cs.umass.edu/vidit/IndianFaceDatabase (2002)

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

Personalised recommendations

Cite chapter

Buy options