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JACIII Vol.11 No.2 pp. 195-201
doi: 10.20965/jaciii.2007.p0195
(2007)

Paper:

New Fast Principal Component Analysis for Face Detection

Hazem M. El-Bakry

Faculty of Computer Science & Information Systems, Mansoura University, Egypt

Received:
March 19, 2006
Accepted:
August 29, 2006
Published:
February 20, 2007
Keywords:
fast PCA, face detection, cross correlation, frequency domain
Abstract

Principal component analysis (PCA) has different important applications, especially in pattern detection such as face detection and recognition. In real-time applications, response time must be as fast as possible. For this, we propose a new PCA implementation for fast face detection based on the cross-correlation in the frequency domain between the input image and eigenvectors (weights). Simulation results demonstrate that our proposal is faster than the conventional one, and experimental results for different images show good performance.

Cite this article as:
Hazem M. El-Bakry, “New Fast Principal Component Analysis for Face Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.2, pp. 195-201, 2007.
Data files:
References
  1. [1] L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America A, 4(3), pp. 519-524, 1987.
  2. [2] M. Kirby and L. Sirovich, “Application of the Karhunen-Loeve procedure for the characterization of human faces,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 12(1), 1990.
  3. [3] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol.3, No.1, pp. 71-86, 1991.
  4. [4] www.Cs.Unchicagio.edu/˜qingj/thesis/index.htm ,
    “Principle Component Analysis and Neural Network Based Face Recognition.”
  5. [5] J. A. Anderson, J. W. Silverstein, S. A. Ritz, R. S. Jones, and T. Kohonen, “Discriminative features, categorical perception, and probability learning: some applications of a neural model,” Psychological Review, No.84, pp. 413-451, 1977.
  6. [6] T. Kohonen, “Associative Memory: A system theoretic approach,” Berlin: Springer-Verlag, 1977.
  7. [7] H. M. El-Bakry and Q. Zhao, “Fast Normalized Neural Processors For Pattern Detection Based on Cross Correlation Implemented in the Frequency Domain,” Journal of Research and Practice in Information Technology, Vol.38, No.2, pp. 151-170, May, 2006.
  8. [8] H. M. El-Bakry and Q. Zhao, “Speeding-up Normalized Neural Networks For Face/Object Detection,” Machine Graphics & Vision Journal (MG&V), Vol.14, No.1, pp. 29-59, 2005.
  9. [9] H. M. El-Bakry and Q. Zhao, “Fast Time Delay Neural Networks,” the International Journal of Neural Systems, Vol.15, No.6, pp. 445-455, December, 2005.
  10. [10] H. M. El-Bakry and Q. Zhao, “A New Technique for Fast Pattern Recognition Using Normalized Neural Networks,” WSEAS Transactions on Information Science and Applications, Issue 11, Vol.2, pp. 1816-1835, November, 2005.
  11. [11] H. M. El-Bakry and Q. Zhao, “Fast Pattern Detection Using Normalized Neural Networks and Cross Correlation in the Frequency Domain,” EURASIP Journal on Applied Signal Processing, Special Issue on Advances in Intelligent Vision Systems: Methods and Applications – Part I, Vol.2005, No.13, pp. 2054-2060, August 1, 2005.
  12. [12] H. M. El-Bakry and Q. Zhao, “A Fast Neural Algorithm for Serial Code Detection in a Stream of Sequential Data,” International Journal of Information Technology, Vol.2, No.1, pp. 71-90, 2005.
  13. [13] H. M. El-bakry and Q. Zhao, “Modified Time Delay Neural Networks For Fast Data Processing,” Proc. of IEEE Eighth International Symposium on Signal Processing and its Applications, Sydney, Australia, August 28-31, 2005.
  14. [14] H. M. El-Bakry, “Human Iris Detection Using Fast Cooperative Modular Neural Nets and Image Decomposition,” Machine Graphics & Vision Journal (MG&V), Vol.11, No.4, pp. 498-512, 2002.
  15. [15] H. M. El-Bakry, “New High Speed Normalized Neural Networks for Fast Pattern Discovery on Web Pages,” the International Journal of Computer Science and Network Security, Vol.6, No.2A, pp. 142-152, February, 2006.
  16. [16] H. M. El-Bakry, “Face detection using fast neural networks and image decomposition,” Neurocomputing Journal, Vol.48, pp. 1039-1046, 2002.
  17. [17] H. M. El-Bakry, “Automatic Human Face Recognition Using Modular Neural Networks,” Machine Graphics & Vision Journal (MG&V), Vol.10, No.1, pp. 47-73, 2001.
  18. [18] H. M. El-Bakry, “A New High Speed Neural Model For Character Recognition Using Cross Correlation and Matrix Decomposition,” International Journal of Signal Processing, Vol.2, No.3, pp. 183-202, 2005.
  19. [19] R. Klette and Zamperon, “Handbook of image processing operators,” John Wiley & Sons, Ltd., 1996.
  20. [20] J. W. Cooley and J. W. Tukey, “An algorithm for the machine calculation of complex Fourier series,” Math. Comput., 19, pp. 297-301, 1965.
  21. [21] J. P. Lewis, “Fast Normalized Cross Correlation,”
    Available from http://www.idiom.com/˜zilla/
  22. [22] www.Cs.Unchicagio.edu/˜qingj/thesis/index.htm ,
    “Principle Component Analysis and Neural Network Based Face Recognition.”
  23. [23] H. M. El-Bakry, “New Fast Time Delay Neural Networks Using Cross Correlation Performed in the Frequency Domain,” Neurocomputing Journal, Vol.69, pp. 2360-2363, October, 2006.

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