Advertisement

Circuits, Systems, and Signal Processing

, Volume 38, Issue 4, pp 1684–1694 | Cite as

Mixed Nonorthogonal Transforms Representation for Face Recognition

  • Taif AlobaidiEmail author
  • Wasfy B. Mikhael
Article
  • 26 Downloads

Abstract

An alternative face recognition system that additively combines two-dimensional discrete wavelet transform (2D-DWT) coefficients and two-dimensional discrete cosine transform (2D-DCT) coefficients for image feature extraction is proposed. Each training pose is represented by superimposing the dominant coefficients from the two domains taking into account the nonorthogonality of the coefficients in one domain with respect to the coefficients in the other domain. The recognition system is tested with three publicly available databases, namely ORL, YALE, and FERET. As shown in the sample results, the proposed system significantly reduces the required storage size, a desirable property for big data and when computing resources are limited, while maintaining the accuracy of recognition rates when compared with the 2D-DCT, the 2D-DWT, and the successive 2D-DWT/2D-DCT techniques. In addition, the computational complexity in the testing phase is comparable with that of recently reported techniques.

Keywords

Mixed transforms Discrete cosine transform Discrete wavelet transform Face recognition 

Notes

Acknowledgements

The authors acknowledge the University of Central Florida Advanced Research Computing Center for providing computational resources that contributed to results reported herein. URL: https://arcc.ist.ucf.edu. Also, the authors would like to thank Mr. André Beckus for his valuable comments.

References

  1. 1.
    W. Burger, M.J. Burge, Digital Image Processing: An Algorithmic Introduction Using Java (Springer, Berlin, 2009)zbMATHGoogle Scholar
  2. 2.
    R. Das, Adopting Biometric Technology: Challenges and Solutions (CRC Press, Boca Raton, 2016)CrossRefGoogle Scholar
  3. 3.
    O. Database, AT&T laboratories Cambridge database of faces (April 1992–1994). http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html. Accessed 16 Aug 2018
  4. 4.
    Y. Database, UCSD computer vision. http://vision.ucsd.edu/content/yale-face-database. Accessed 16 Aug 2018
  5. 5.
    C. Davis, The norm of the Schur product operation. Numer. Math. 4(1), 343–344 (1962)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    R.C. Gonzalez, R.E. Woods, Digital Image Processing (Prentice Hall, Upper Saddle River, 2002)Google Scholar
  7. 7.
    A.T.S. Ho, S. Li, Handbook of Digital Forensics of Multimedia Data and Devices (Wiley, Chicester, 2015)CrossRefGoogle Scholar
  8. 8.
    S. Mallat, A Wavelet Tour of Signal Processing (Elsevier, Amsterdam, 1999)zbMATHGoogle Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    A. Ramaswamy, W.B. Mikhael, Multitransform/multidimensional signal representation, in 1993 IEEE 36th Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1255–1258 (1993)Google Scholar
  12. 12.
    S. Rao, M.V.B. Rao, A novel triangular DCT feature extraction for enhanced face recognition, in 2016 IEEE 10th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6 (2016)Google Scholar
  13. 13.
    K. Sayood, Introduction to Data Compression (Morgan Kaufmann, Burlington, 2017)zbMATHGoogle Scholar
  14. 14.
    L. Verdoliva, Handbook of digital forensics of multimedia data and devices [Book Reviews]. IEEE Signal Process. Mag. 33(1), 164–165 (2016).  https://doi.org/10.1109/MSP.2015.2488018 CrossRefGoogle Scholar
  15. 15.
    M. Wang, H. Jiang, Y. Li, Face recognition based on DWT/DCT and SVM, in 2010 International Conference on Computer Application and System Modeling (ICCASM), pp. V3-507–V3-510 (2010).  https://doi.org/10.1109/ICCASM.2010.5620666
  16. 16.
    B. Widrow, J. McCool, A comparison of adaptive algorithms based on the methods of steepest descent and random search. IEEE Trans. Antennas Propag. 24(5), 615–637 (1976).  https://doi.org/10.1109/TAP.1976.1141414 MathSciNetCrossRefGoogle Scholar
  17. 17.
    F. Zhi-Peng, Z. Yan-Ning, H. Hai-Yan, Survey of deep learning in face recognition, in 2014 International Conference on Orange Technologies (ICOT), pp. 5–8 (2014).  https://doi.org/10.1109/ICOT.2014.6954663

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

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

Personalised recommendations