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A Comparative Study of Fractal Dimension Based Age Group Classification of Facial Images with Different Testing Strategies

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 327)

Abstract

The demand of estimation of age from facial images has tremendous applications in real world scenario like law enforcement, security control, and human computer interaction etc. However despite advances in automatic age estimation, the computer based age classification has become prevalent. The present paper evaluates the method of age group classification based on the Correlation Fractal Dimension (FD) of facial image using different validation techniques. To reduce variability, multiple rounds of cross validation are performed using different partitions to the data. The expected level of fit of the model classifying facial images into four categories based on FD value of a facial edge is estimated using multiple cross-validation techniques. The simulation is carried out and results are analyzed on different images from FG-NET database, Google database and from the scanned photographs as these are random in nature and help to indicate the efficiency and reliability of the proposed method. It is also a successful demonstration that Correlation Fractal Dimension of a facial edge is sufficient for a classification task with high percentage of classification accuracy.

Keywords

Age Group classification Correlation Fractal Dimension facial image canny edge facial edge image cross validation 

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References

  1. 1.
    Li, S.Z., Jain, A.K. (eds.): Handbook of face recognition, 2nd edn. Springer, London (2011)Google Scholar
  2. 2.
    Kumar, N., Berg, A., Belhumeur, P., Nayar, S.: Describable visual attributes for face verification and image search. IEEE Transaction on PAMI 33(10), 1962–1977 (2011)CrossRefGoogle Scholar
  3. 3.
    Atkinson, P.M., Lewis, P.: Geostatistical classification for remote sensing: An introduction. Computers and Geosciences 26, 361–371 (2000)CrossRefGoogle Scholar
  4. 4.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  6. 6.
    Chandra, M., VijayaKumar, V., Damodaram, A.: Adulthood classification based on geometrical facial features. ICGST (2009)Google Scholar
  7. 7.
    Fu, Y., Huang, T.: Human age estimation with regression on discriminative aging manifold. IEEE Trans. Multimedia 10(4), 578–584 (2008)CrossRefGoogle Scholar
  8. 8.
    Chao, W.-L., Liu, J.-Z., Ding, J.-J.: Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recognition 46(3), 628–641 (2013)CrossRefGoogle Scholar
  9. 9.
    Chang, K.-Y., Chen, C.-S., Hung, Y.-P.: Ordinal hyperplanes ranker with cost sensitivities for age estimation. In: IEEE CVPR, pp. 585–592 (2011)Google Scholar
  10. 10.
    Choi, S.E., Lee, Y.J., Lee, S.J., Park, K.R., Kim, J.: Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition 44(6), 1262–1281 (2011)CrossRefzbMATHGoogle Scholar
  11. 11.
    Wen-Bing, H., Cheng-Ping, L., Chun-Wen, C.: Classification of Age Groups Based on Facial Features. Tamkang Journal of Science and Engineering 4(3), 183–192 (2001)Google Scholar
  12. 12.
    Young, H.K., Niels-da-Vitoria, L.: Age Classification from Facial Images. Computer Vision and Image Understanding 74(1), 1–21 (1999)CrossRefGoogle Scholar
  13. 13.
    Todd, J.T., Mark, L.S., Shaw, R.E., Pittenger, J.B.: The perception of human growth. Scientific American 242(2), 132–144 (1980)CrossRefGoogle Scholar
  14. 14.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human face. J. Opt. Am. A 7(3), 519–524 (1987)CrossRefGoogle Scholar
  15. 15.
    Hasegawa, H., Simizu, E.: Discrimination of facial age generation using neural networks. T. IEE Japan 117-C(12), 1897–1898 (1997)Google Scholar
  16. 16.
    Kosugi, M.: Human-face recognition using mosaic pattern and neural networks. IEICE Trans. J76-D-II(6), 1132–1139 (1993)Google Scholar
  17. 17.
    Anuradha, Y., Murthy, J.V.R., Krishnaprasad, M.H.M.: A Novel Method for Human Age Group Classification based on Correlation Fractal Dimension of Facial Edges. Communicated to International Journal of Saud Arab (Elsevier publication) and is Under Review (2014)Google Scholar
  18. 18.
    Canny, J.F.: Finding edges and lines in image, Master’s thesis, MIT (1983)Google Scholar
  19. 19.
    Canny, J.F.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Machine Intell. PAMI-8(6), 679–697 (1986)Google Scholar
  20. 20.
    Raman, M., Himanshu, A.: Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing 3(1), 1–11 (2009)CrossRefGoogle Scholar
  21. 21.
    Mandelbrot, B.B.: The Fractal Geometry of Nature. W.H.Freeman, New York (1982)Google Scholar
  22. 22.
    Matthews, J.: An introduction to edge detection: The sobel edge detector (2002)Google Scholar
  23. 23.
    Pentland, A.P.: Fractal-Based Description of Natural Scenes. IEEE PAM I -6(6) (1984)Google Scholar
  24. 24.
    Paul, S.A.: Fractal and chaos. IOP publishing (2005)Google Scholar
  25. 25.
    Anuradha, Y., Murthy, J.V.R., Krishnaprasad, M.H.M.: Estimating Correlation Dimension using Multi Layered Grid and Damped Window Model over Data Streams. Elsevier Procedia Technology 10, 797–804 (2013)CrossRefGoogle Scholar
  26. 26.
    Sujatha, B., Vijayakumar, V., Rama, B.M.: Morphological Primitive Patterns with Grain Components on LDP for Child and Adult Age Classification. International Journal of Computer Applications 21(3), 0975-8887 (2011)Google Scholar
  27. 27.
    Yazdi, M., Mardani-Samani, S., Bordbar, M., Mobaraki, R.: Age Classification based on RBF Neural Network. Canadian Journal on Image Processing and Computer Vision 3(2), 38–42 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of CSEJNTUHHyderabadIndia
  2. 2.Department of CSE,UCEKJNTUKKakinadaIndia

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