Circuits, Systems, and Signal Processing

, Volume 37, Issue 5, pp 2045–2073 | Cite as

Face Recognition Employing DMWT Followed by FastICA

  • Ahmed Aldhahab
  • Wasfy B. Mikhael


Face recognition becomes a challenging topic in several fields since images of faces are varied by changing illuminations, facial rotations, facial expressions, etc. In this paper, two dimensional discrete multiwavelet transform (2D DMWT) and fast independent component analysis (FastICA) are proposed for face recognition. Preprocessing, feature extraction, and classification are the main steps in the proposed system. In the preprocessing step, each pose in the database is divided into six parts to reduce the effect of unnecessary facial features and highlight the local features in each part. For feature extraction, the 2D DMWT is applied to each part for dimensionality reduction and features extraction. This results in two facial representations. Then FastICA followed by \(\ell _2\)-norm is applied to each representation, which produces six and three different techniques for the first and second representation, respectively. This results in features that are more discriminating, less dependent, and more compressed. In the recognition step, the resulted compressed features from the two representations are fed to a neural network-based classifier for training and testing. The proposed techniques are extensively evaluated using five databases, namely ORL, YALE, FERET, FEI, and LFW, which have different facial variations, such as illuminations, rotations, facial expressions, etc. The results are analyzed using K-fold cross-validation. Sample results and comparison with a large number of recently proposed approaches are provided. The proposed approach is shown to yield significant improvement compared with the other approaches.


Discrete wavelet and multiwavelet transform Independent component analysis (ICA) FastICA Neural network Face recognition 



This work was supported by the Iraqi government scholarship (HCED). The authors acknowledge the valuable comments and feedback from their colleague “Dr. George Atia”.


  1. 1.
    T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    S. Ajitha, A.A. Fathima, V. Vaidehi, M. Hemalatha, R. Karthigaiveni, Face recognition system using combined gabor wavelet and DCT approach, in IEEE International Conference on Recent Trends in Information Technology (ICRTIT) (2014), pp. 1–6Google Scholar
  3. 3.
    A. Aldhahab, G. Atia, W.B. Mikhael, Supervised facial recognition based on multi-resolution analysis and feature alignment, in 2014 IEEE 57th International Midwest Symposium on Circuits and Systems (MWSCAS) (2014), pp. 137–140Google Scholar
  4. 4.
    A. Aldhahab, G. Atia, W.B. Mikhael, High performance and efficient facial recognition using norm of ICA/multiwavelet features, in Proceedings of the Advances in Visual Computing: 11th International Symposium, Part II, ISVC 2015, Las Vegas, NV, USA, 14–16, December 2015, ed. By G. Bebis, R. Boyle, B. Parvin, D. Koracin, I. Pavlidis, R. Feris, T. McGraw, M. Elendt, R. Kopper, E. Ragan, Z. Ye, G. Weber (Springer International Publishing, Cham, 2015), pp. 672–681Google Scholar
  5. 5.
    A. Aldhahab, G. Atia, W.B. Mikhael, Supervised facial recognition based on eigenanalysis of multiresolution and independent features, in IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS) (IEEE, 2015), pp. 1–4Google Scholar
  6. 6.
    S. Arlot, A. Celisse et al., A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    M.E. Ashalatha, M.S. Holi, P.R. Mirajkar, Face recognition using local features by LPP approach, in IEEE International Conference on Circuits, Communication, Control and Computing, vol. I4C (2014), pp. 382–386Google Scholar
  8. 8.
    M. Bartlett, J.R. Movellan, T. Sejnowski, Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  9. 9.
    I.J. Brown, A wavelet tour of signal processing: the sparse way. Investig. Oper. 1, 85 (2009)Google Scholar
  10. 10.
    F.Z. Chelali, A. Djeradi, N. Cherabit, Investigation of DCT/PCA combined with Kohonen classifier for human identification, in IEEE 4th International Conference on Electrical Engineering (ICEE) (2015), pp. 1–7Google Scholar
  11. 11.
    K.W. Cheung, L.M. Po, Preprocessing for discrete multiwavelet transform of two-dimensional signals, in Proceedings IEEE International Conference on Image Processing, vol. 2 (IEEE, 1997), pp. 350–353Google Scholar
  12. 12.
    Y.T. Chou, J.F. Yang, Intra-facial-feature canonical correlation analysis for face recognition, in TENCON 2015–2015 IEEE Region 10 Conference (2015), pp. 1–4. doi: 10.1109/TENCON.2015.7372961
  13. 13.
    A. Dahmouni, N. Aharrane, K.E. Moutaouakil, K. Satori, Face recognition using local binary probabilistic pattern (LBPP) and 2D-DCT frequency decomposition, in IEEE 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV) (2016), pp. 73–77Google Scholar
  14. 14.
    O. Database, At&t laboratories Cambridge database of faces (April 1992–1994).
  15. 15.
    Y. Database, Ucsd computer vision.
  16. 16.
    B. Dhivakar, C. Sridevi, S. Selvakumar, P. Guhan, Face detection and recognition using skin color, in IEEE 3rd International Conference on Signal Processing, Communication and Networking (ICSCN) (2015), pp. 1–7Google Scholar
  17. 17.
    C. Ding, D. Tao, Robust face recognition via multimodal deep face representation. IEEE Trans. Multimed. 17(11), 2049–2058 (2015)CrossRefGoogle Scholar
  18. 18.
    B.A. Draper, K. Baek, M.S. Bartlett, J.R. Beveridge, Recognizing faces with PCA and ICA. Comput. Vis. Image Underst. 91(1), 115–137 (2003)CrossRefGoogle Scholar
  19. 19.
    X. Duan, Z.H. Tan, Local feature learning for face recognition under varying poses, in IEEE International Conference on Image Processing (ICIP) (2015), pp. 2905–2909Google Scholar
  20. 20.
    S. Farokhi, U.U. Sheikh, J. Flusser, B. Yang, Near infrared face recognition using Zernike moments and Hermite kernels. Inf. Sci. 316, 234–245 (2015). doi: 10.1016/j.ins.2015.04.030. Nature-Inspired Algorithms for Large Scale Global OptimizationCrossRefGoogle Scholar
  21. 21.
    J.S. Geronimo, D.P. Hardin, P.R. Massopust, Fractal functions and wavelet expansions based on several scaling functions. J. Approx. Theory 78(3), 373–401 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    M. Girolami, C. Fyfe, Blind separation of sources using exploratory projection pursuit networks, in International Conference on the Speech and Signal Processing Engineering Applications of Neural Networks (1996), pp. 249–252Google Scholar
  23. 23.
    R.C. Gonzalez, R.E. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, 2002)Google Scholar
  24. 24.
    P.D.B. Harrington, Sigmoid transfer functions in backpropagation neural networks. Anal. Chem. 65(15), 2167–2168 (1993)CrossRefGoogle Scholar
  25. 25.
    G.B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Tech. Rep. 07-49 (University of Massachusetts, Amherst, 2007)Google Scholar
  26. 26.
    A. Hyvarinen, New approximations of differential entropy for independent component analysis and projection pursuit. Adv. Neural Inf. Process. Syst. 10(2), 273–279 (1998)Google Scholar
  27. 27.
    A. Hyvarinen, Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)CrossRefGoogle Scholar
  28. 28.
    A. Hyvärinen, E. Oja, A fast fixed-point algorithm for independent component analysis. Neural Comput. 9(7), 1483–1492 (1997)CrossRefGoogle Scholar
  29. 29.
    A. Iosifidis, A. Tefas, I. Pitas, On the optimal class representation in linear discriminant analysis. IEEE Trans. Neural Netw. Learn. Syst. 24(9), 1491–1497 (2013)CrossRefGoogle Scholar
  30. 30.
    R. Jafri, H.R. Arabnia, A survey of face recognition techniques. J. Inf. Process. Syst. 5(2), 41–68 (2009)CrossRefGoogle Scholar
  31. 31.
    M. Johnson, A. Savakis, Fast l1-eigenfaces for robust face recogntion, in IEEE Western New York Image and Signal Processing Workshop (WNYISPW) (2014), pp. 1–5Google Scholar
  32. 32.
    J. Karhunen, P. Pajunen, E. Oja, The nonlinear PCA criterion in blind source separation: relations with other approaches. Neurocomputing 22(1), 5–20 (1998)CrossRefzbMATHGoogle Scholar
  33. 33.
    T. Khadhraoui, S. Ktata, F. Benzarti, H. Amiri, Features selection based on modified PSO algorithm for 2D face recognition, in IEEE 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV) (2016), pp. 99–104Google Scholar
  34. 34.
    S. Kundu, P.P. Markopoulos, D.A. Pados, Fast computation of the l1-principal component of real-valued data, in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014), pp. 8028–8032Google Scholar
  35. 35.
    E. Kussul, T. Baydyk, Face recognition using special neural networks, in IEEE International Joint Conference on Neural Networks (IJCNN) (2015), pp. 1–7Google Scholar
  36. 36.
    D.D. Lee, H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefzbMATHGoogle Scholar
  37. 37.
    Z. Lei, D. Yi, S.Z. Li, Learning stacked image descriptor for face recognition. IEEE Trans. Circuits Syst. Video Technol. PP(99), 1 (2015)Google Scholar
  38. 38.
    D. Li, H. Zhou, K.M. Lam, High-resolution face verification using pore-scale facial features. IEEE Trans. Image Process. 24(8), 2317–2327 (2015)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Y. Linde, A. Buzo, R. Gray, An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1), 84–95 (1980)CrossRefGoogle Scholar
  40. 40.
    X. Long, H. Lu, Y. Peng, Sparse non-negative matrix factorization based on spatial pyramid matching for face recognition, in IEEE 5th International Conference on Intelligent Human–Machine Systems and Cybernetics (IHMSC), vol. 1 (2013), pp. 82–85Google Scholar
  41. 41.
    M. Luo, L. Song, S.D. Li, An improved face recognition based on ICA and WT, in IEEE Asia-Pacific Services Computing Conference (APSCC) (2012), pp. 315–318Google Scholar
  42. 42.
    S.M. Mahbubur Rahman, S.P. Lata, T. Howlader, Bayesian face recognition using 2D Gaussian–Hermite moments. EURASIP J. Image Video Process. 2015(1), 35 (2015). doi: 10.1186/s13640-015-0090-5 CrossRefGoogle Scholar
  43. 43.
    S.S. Mudholkar, P.M. Shende, M. Sarode, Biometrics authentication technique for intrusion detection system using fingerprint recognition. Int. J. Comput. Sci. Eng. Inf. Technol. 2(1), 57–65 (2012)Google Scholar
  44. 44.
    M.M. Mukhedkar, S.B. Powalkar, Fast face recognition based on wavelet transform on PCA, in IEEE International Conference on Energy Systems and Applications (2015), pp. 761–764Google Scholar
  45. 45.
    S.J. Natu, P.J. Natu, T.K. Sarode, H. Kekre, Performance comparison of face recognition using DCT against face recognition using vector quantization algorithms LBG, KPE, KMCG, KFCG. Int. J. Image Process. (IJIP) 4(4), 377–389 (2010)Google Scholar
  46. 46.
    J.S. Pan, Q. Feng, L. Yan, J.F. Yang, Neighborhood feature line segment for image classifications. IEEE Trans. Circuits Syst. Video Technol. 25(3), 387–398 (2015)CrossRefGoogle Scholar
  47. 47.
    K. Papachristou, A. Tefas, I. Pitas, Symmetric subspace learning for image analysis. IEEE Trans. Image Process. 23(12), 5683–5697 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  48. 48.
    P.J. Phillips, H. Moon, S. Rizvi, P.J. Rauss et al., The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  49. 49.
    P.J. Phillips, H. Wechsler, J. Huang, P.J. Rauss, The feret database and evaluation procedure for face-recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  50. 50.
    S.M. Rahman, T. Howlader, D. Hatzinakos, On the selection of 2D krawtchouk moments for face recognition. Pattern Recogn. 54, 83–93 (2016). doi: 10.1016/j.patcog.2016.01.003 CrossRefGoogle Scholar
  51. 51.
    J. Soldera, C.A.R. Behaine, J. Scharcanski, Customized orthogonal locality preserving projections with soft-margin maximization for face recognition. IEEE Trans. Instrum. Meas. 64(9), 2417–2426 (2015)CrossRefGoogle Scholar
  52. 52.
    V. Strela, Multiwavelets: theory and applications. Ph.D. Thesis (Citeseer, 1996)Google Scholar
  53. 53.
    V. Strela, P. Heller, G. Strang, P. Topiwala, C. Heil, The application of multiwavelet filterbanks to image processing. IEEE Trans. Image Process. 8(4), 548–563 (1999)CrossRefGoogle Scholar
  54. 54.
    V. Strela, A.T. Walden, Orthogonal and biorthogonal multiwavelets for signal denoising and image compression, in Aerospace/Defense Sensing and Controls (International Society for Optics and Photonics, 1998), pp. 96–107Google Scholar
  55. 55.
    C.E. Thomaz, G.A. Giraldi, A new ranking method for principal components analysis and its application to face image analysis. Image Vis. Comput. 28(6), 902–913 (2010).
  56. 56.
    M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  57. 57.
    L. Wiskott, J.M. Fellous, N. Kuiger, C. von der Malsburg, Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)CrossRefGoogle Scholar
  58. 58.
    X. Yu, D. Hu, J. Xu, Blind Source Separation: Theory and Applications (Wiley, Singapore, 2014)CrossRefGoogle Scholar
  59. 59.
    P.C. Yuen, J.H. Lai, Face representation using independent component analysis. Pattern Recogn. 35(6), 1247–1257 (2002)CrossRefzbMATHGoogle Scholar
  60. 60.
    J.J. Zhang, Y.T. Shi, Face recognition systems based on independent component analysis and support vector machine, in IEEE International Conference on Audio, Language and Image Processing (ICALIP) (2014), pp. 296–300Google Scholar
  61. 61.
    Z. Zhang, J. Li, R. Zhu, Deep neural network for face recognition based on sparse autoencoder, in IEEE 8th International Congress on Image and Signal Processing (CISP) (2015), pp. 594–598Google Scholar
  62. 62.
    X. Zhihua, L. Guodong, Weighted infrared face recognition in multiwavelet domain, in IEEE International Conference on Imaging Systems and Techniques (IST) (2013), pp. 70–74Google Scholar
  63. 63.
    X. Zhu, Z. Lei, J. Yan, D. Yi, S.Z. Li, High-fidelity pose and expression normalization for face recognition in the wild, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), pp. 787–796Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of Central FloridaOrlandoUSA
  2. 2.Department of Electrical EngineeringUniversity of BabylonHillaIraq

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